MD2K Publications

@inbook{Liu2017,
pages = "361--387",
title = "Learning Continuous-Time Hidden Markov Models for Event Data",
publisher = "Springer International Publishing",
year = 2017,
author = "Liu, Yu-Ying and Moreno, Alexander and Li, Shuang and Li, Fuxin and Song, Le and Rehg, James M.",
editor = "Rehg, James M. and Murphy, Susan A. and Kumar, Santosh",
address = "Cham",
isbn = "978-3-319-51394-2",
__markedentry = "[bbwillms:6]",
abstract = "The Continuous-Time Hidden Markov Model (CT-HMM) is an attractive modeling tool for mHealth data that takes the form of events occurring at irregularly-distributed continuous time points. However, the lack of an efficient parameter learning algorithm for CT-HMM has prevented its widespread use, necessitating the use of very small models or unrealistic constraints on the state transitions. In this paper, we describe recent advances in the development of efficient EM-based learning methods for CT-HMM models. We first review the structure of the learning problem, demonstrating that it consists of two challenges: (1) the estimation of posterior state probabilities and (2) the computation of end-state conditioned expectations. The first challenge can be addressed by reformulating the estimation problem in terms of an equivalent discrete time-inhomogeneous hidden Markov model. The second challenge is addressed by exploiting computational methods traditionally used for continuous-time Markov chains and adapting them to the CT-HMM domain. We describe three computational approaches and analyze the tradeoffs between them. We evaluate the resulting parameter learning methods in simulation and demonstrate the use of models with more than 100 states to analyze disease progression using glaucoma and Alzheimer's Disease datasets.",
booktitle = "Mobile Health: Sensors, Analytic Methods, and Applications",
doi = "10.1007/978-3-319-51394-2_19",
url = "https://doi.org/10.1007/978-3-319-51394-2_19"
}

@inbook{Parate2017,
pages = "175--201",
title = "Detecting Eating and Smoking Behaviors Using Smartwatches",
publisher = "Springer International Publishing",
year = 2017,
author = "Parate, Abhinav and Ganesan, Deepak",
editor = "Rehg, James M. and Murphy, Susan A. and Kumar, Santosh",
address = "Cham",
isbn = "978-3-319-51394-2",
__markedentry = "[bbwillms:]",
abstract = "Inertial sensors embedded in commercial smartwatches and fitness bands are among the most informative and valuable on-body sensors for monitoring human behavior. This is because humans perform a variety of daily activities that impacts their health, and many of these activities involve using hands and have some characteristic hand gesture associated with it. For example, activities like eating food or smoking a cigarette require the direct use of hands and have a set of distinct hand gesture characteristics. However, recognizing these behaviors is a challenging task because the hand gestures associated with these activities occur only sporadically over the course of a day, and need to be separated from a large number of irrelevant hand gestures. In this chapter, we will look at approaches designed to detect behaviors involving sporadic hand gestures. These approaches involve two main stages: (1) spotting the relevant hand gestures in a continuous stream of sensor data, and (2) recognizing the high-level activity from the sequence of recognized hand gestures. We will describe and discuss the various categories of approaches used for each of these two stages, and conclude with a discussion about open questions that remain to be addressed.",
booktitle = "Mobile Health: Sensors, Analytic Methods, and Applications",
doi = "10.1007/978-3-319-51394-2_10",
url = "https://doi.org/10.1007/978-3-319-51394-2_10"
}

@inbook{Gao2017,
pages = "289--312",
title = "A New Direction for Biosensing: RF Sensors for Monitoring Cardio-Pulmonary Function",
publisher = "Springer International Publishing",
year = 2017,
author = "Gao, Ju and Baskar, Siddharth and Teng, Diyan and al'Absi, Mustafa and Kumar, Santosh and Ertin, Emre",
editor = "Rehg, James M. and Murphy, Susan A. and Kumar, Santosh",
address = "Cham",
isbn = "978-3-319-51394-2",
abstract = "Long-term monitoring of physiology at large-scale can help determine potential causes and early biomarkers of chronic diseases. Physiological monitoring today, however, requires wearing of sensors such as electrodes for ECG and belt around lungs for respiration, and is unsuitable for monitoring of patients and healthy adults over multiple years. In this chapter, we review advances in a novel sensing modality using radio frequency (RF) waves that can provide physiological measurements without skin contact in both lab and field environments. This chapter presents fundamentals of RF biosensing with experimental results of a new experimental bioradar platform illustrating the concepts. The focus is on new approaches to monitor heart motion and respiratory effort. Experimental results using both an articulated heart phantom and human subjects show that RF sensing modality can match the performance of state-of-the-art physiological monitoring devices in terms of retrieving features and statistics of clinical significance.",
booktitle = "Mobile Health: Sensors, Analytic Methods, and Applications",
doi = "10.1007/978-3-319-51394-2_15",
url = "https://doi.org/10.1007/978-3-319-51394-2_15"
}

@inbook{Nilsen2017,
pages = "443--453",
title = "Modeling Opportunities in mHealth Cyber-Physical Systems",
publisher = "Springer International Publishing",
year = 2017,
author = "Nilsen, Wendy and Ertin, Emre and Hekler, Eric B. and Kumar, Santosh and Lee, Insup and Mangharam, Rahul and Pavel, Misha and Rehg, James M. and Riley, William and Rivera, Daniel E. and Spruijt-Metz, Donna",
editor = "Rehg, James M. and Murphy, Susan A. and Kumar, Santosh",
address = "Cham",
isbn = "978-3-319-51394-2",
abstract = "Cyber-physical systems, with their focus on creating closed-loop systems, have transformed a wide range of areas (e.g., flight systems, industrial plants, robotics, etc.). However, even after a century of health research we still lack dynamic computational models of human health and its interactions with the environment, let alone a full closed-loop cyber-physical system. A major hurdle to developing cyber-physical systems in the medical and health fields has been the lack of high-resolution data on changes in both outcomes and predictive variables in the natural environment. There are many public and private initiatives addressing these measurement issues and the health research community is witnessing rapid progress in this area. Consequently, there is an emerging opportunity to develop cyber-physical systems for mobile health (mHealth). This chapter describes research challenges in developing cyber-physical system models to build effective and safe mHealth interventions. Doing so involves significant advances in modeling of health, biology, and behavior and their interactions with the environment and response of humans to the mHealth interventions.",
booktitle = "Mobile Health: Sensors, Analytic Methods, and Applications",
doi = "10.1007/978-3-319-51394-2_23",
url = "https://doi.org/10.1007/978-3-319-51394-2_23"
}

@inproceedings{ho2017emu,
author = "Bo-Jhang Ho and Bharathan Balaji and Nima Nikzad and Mani Srivastava",
title = "Emu: Engagement Modeling for User Studies",
booktitle = "UbiTtention 2017: 2nd International Workshop on Smart \& Ambient Notification and Attention Management",
year = 2017,
organization = "ACM",
abstract = "Mobile technologies that drive just-in-time ecological momentary assessments and interventions provide an unprecedented view into user behaviors and opportunities to manage chronic conditions. The success of these methods rely on engaging the user at the appropriate moment, so as to maximize questionnaire and task completion rates. However, mobile operating systems provide little support to precisely specify the contextual conditions in which to notify and engage the user, and study designers often lack the expertise to build context-aware software themselves. To address this problem, we have developed Emu, a framework that eases the development of context-aware study applications by providing a concise and powerful interface for specifying temporal- and contextual-constraints for task notifications. In this paper we present the design of the Emu API and demonstrate its use in capturing a range of scenarios common to smartphone-based study applications.",
date = "2017-01-01",
pubstate = "published",
tppubtype = "inproceedings"
}

@article{7891193,
title = "Center of Excellence for Mobile Sensor Data-to-Knowledge (MD2K)",
author = "S. Kumar and G. Abowd and W. T. Abraham and M. al'Absi and D. H. Chau and E. Ertin and D. Estrin and D. Ganesan and T. Hnat and S. M. Hossain and Z. Ives and J. Kerr and B. M. Marlin and S. Murphy and J. M. Rehg and I. Nahum-Shani and V. Shetty and I. Sim and B. Spring and M. Srivastava and D. Wetter",
url = "http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7891193",
doi = "10.1109/MPRV.2017.29",
issn = "1536-1268",
year = 2017,
date = "2017-01-01",
journal = "IEEE Pervasive Computing",
volume = 16,
number = 2,
pages = "18-22",
abstract = "The National Center of Excellence for Mobile Sensor Data-to-Knowledge (MD2K) was established in October 2014 with a grant from the National Institutes of Health under the Big Data-to-Knowledge (BD2K) program. Among the 11 centers of excellence originally funded, MD2K's unique contribution is to develop innovative tools to make it easier to gather, analyze, interpret, and capitalize on high-frequency data from mobile sensors. It seeks to facilitate the monitoring of health states and quantify the temporal dynamics of key physical, biological, behavioral, psychological, social, and environmental factors that contribute to the health and disease risks of individuals. MD2K's overarching goal is to reduce the burden that complex chronic disorders place on health and healthcare by making it feasible to detect and predict person-specific disease risk factors ahead of the onset of adverse clinical events, supporting sensor-driven just-in-time interventions. To demonstrate the utility and wide generalizability of the research and tools developed by MD2K, we initially targeted two biomedical applications—improving the success rate for smoking cessation and reducing the number of rehospitalizations in congestive heart failure (CHF). Our two chosen biomedical applications are at the opposite ends of the temporal spectrum of mortality. Smoking is the leading cause of mortality, causing 1 in 5 deaths, but its mortality risk is far in the future. On the other hand, CHF, which is the leading cause of preventable rehospitalization with a readmission rate of 27 percent, has an immediate mortality risk. The first (of three) iterations of these two studies with 75 participants each is currently underway. In addition, the biomedical applications addressed by MD2K have expanded to managing stress, reducing overeating, reducing cocaine use, and improving oral health.",
keywords = "",
pubstate = "published",
tppubtype = "article"
}

B Spring, A Pfammatter and N Alshurafa. First steps into the brave new transdiscipline of mobile health. JAMA Cardiology 2(1):76-78, 2017. URL, DOIBibTeX

@article{alzantot2017sensegen,
author = "Moustafa Alzantot and Supriyo Chakraborty and Mani B. Srivastava",
title = "SenseGen: A Deep Learning Architecture for Synthetic Sensor Data Generation",
journal = "IEEE BICA'17 (co-located with IEEE Percom 2017)",
year = 2017,
abstract = "Our ability to synthesize sensory data that preserves specific statistical properties of the real data has had tremendous implications on data privacy and big data analytics. The synthetic data can be used as a substitute for selective real data segments – that are sensitive to the user – thus protecting privacy and resulting in improved analytics. However, increasingly adversarial roles taken by data recipients such as mobile apps, or other cloud-based analytics services, mandate that the synthetic data, in addition to preserving statistical properties, should also be “difficult to distinguish from the real data. Typically, visual inspection has been used as a test to distinguish between datasets. But more recently, sophisticated classifier models (discriminators), corresponding to a set of events, have also been employed to distinguish between synthesized and real data. The model operates on both datasets and the respective event outputs are compared for consistency. Prior work on data synthesis have often focused on classifiers that are built for features explicitly preserved by the synthetic data. This suggests that an adversary can build classifiers that can exploit a potentially disjoint set of features for differentiating between the two datasets. In this paper, we take a step towards generating sensory data that can pass a deep learning based discriminator model test, and make two specific contributions: first, we present a deep learning based architecture for synthesizing sensory data. This architecture comprises of a generator model, which is a stack of multiple Long-Short-Term-Memory (LSTM) networks and a Mixture Density Network (MDN); second, we use another LSTM network based discriminator model for distinguishing between the true and the synthesized data. Using a dataset of accelerometer traces, collected using smartphones of users doing their daily activities, we show that the deep learning based discriminator model can only distinguish between the real and synthesized traces with an accuracy in the neighborhood of 50%.",
date = "2017-03-13",
publisher = "IEEE",
pubstate = "forthcoming",
tppubtype = "article",
url = "https://arxiv.org/pdf/1701.08886.pdf"
}

Roy J Adams and Benjamin M Marlin. Learning Time Series Detection Models from Temporally Imprecise Labels. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics. 2017. BibTeX

@inproceedings{adams17,
title = "Learning Time Series Detection Models from Temporally Imprecise Labels",
author = "Roy J. Adams and Benjamin M. Marlin",
year = 2017,
date = "2017-04-20",
booktitle = "Proceedings of the 20th International Conference on Artificial Intelligence and Statistics",
abstract = "In this paper, we consider a new low-quality label learning problem: learning time series detection models from temporally imprecise labels. In this problem, the data consist of a set of input time series, and supervision is provided by a sequence of noisy time stamps corresponding to the occurrence of positive class events. Such temporally imprecise labels commonly occur in areas like mobile health research where human annotators are tasked with labeling the occurrence of very short duration events. We propose a general learning framework for this problem that can accommodate different base classifiers and noise models. We present results on real mobile health data showing that the proposed framework significantly outperforms a number of alternatives including assuming that the label time stamps are noise-free, transforming the problem into the multiple instance learning framework, and learning on labels that were manually re-aligned.",
keywords = "machine learning, mobile health, time series",
pubstate = "published",
tppubtype = "inproceedings"
}

@inproceedings{alzantot17,
title = "RSTensorFlow: GPU Enabled TensorFlow for Deep Learning on Commodity Android Devices",
author = "Moustafa Alzantot and Yingnan Wang and Zhengshuang Ren and Mani B. Srivastava",
url = "http://dl.acm.org/citation.cfm?id=3089801&picked=prox&CFID=792094913&CFTOKEN=35071382",
doi = "https://doi.org/10.1145/3089801.3089805",
year = 2017,
date = "2017-06-23",
booktitle = "Proceedings of the 1st International Workshop on Deep Learning for Mobile Systems and Applications",
abstract = "Mobile devices have become an essential part of our daily lives. By virtue of both their increasing computing power and the recent progress made in AI, mobile devices evolved to act as intelligent assistants in many tasks rather than a mere way of making phone calls. However, popular and commonly used tools and frameworks for machine intelli-gence are still lacking the ability to make proper use of the available heterogeneous computing resources on mobile devices. In this paper, we study the beneﬁts of utilizing the heterogeneous (CPU and GPU) computing resources available on commodity android devices while running deep learning models. We leveraged the heterogeneous comput-ing framework RenderScript to accelerate the execution of deep learning models on commodity Android devices. Our system is implemented as an extension to the popular open-source framework TensorFlow. By integrating our acceler-ation framework tightly into TensorFlow, machine learning engineers can now easily make beneﬁt of the heterogeneous computing resources on mobile devices without the need of any extra tools. We evaluate our system on diﬀerent android phones models to study the trade-oﬀs of running diﬀerent neural network operations on the GPU. We also compare the performance of running diﬀerent models architectures such as convolutional and recurrent neural networks on CPU only vs using heterogeneous computing resources. Our result shows that although GPUs on the phones are capable of of-fering substantial performance gain in matrix multiplication on mobile devices. Therefore, models that involve multi-plication of large matrices can run much faster (approx. 3 times faster in our experiments) due to GPU support.",
keywords = "android, Convolution, deep learning, hetero-geneous computing, LSTM, Neural networks, RenderScript, TensorFlow",
pubstate = "published",
tppubtype = "inproceedings"
}

@inproceedings{Dadkhahi17,
title = "Learning Tree-Structured Detection Cascades for Heterogeneous Networks of Embedded Devices",
author = "Hamid Dadkhahi and Benjamin Marlin",
year = 2017,
date = "2017-08-13",
booktitle = "Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",
journal = "23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining., 2017",
abstract = "In this paper, we present a new approach to learning cascaded classifiers for use in computing environments that involve networks of heterogeneous and resource-constrained, low-power embedded compute and sensing nodes. We present a generalization of the classical linear detection cascade to the case of tree-structured cascades where different branches of the tree execute on different physical compute nodes in the network. Different nodes have access to different features, as well as access to potentially different computation and energy resources. We concentrate on the problem of jointly learning the parameters for all of the classifiers in the cascade given a fixed cascade architecture and a known set of costs required to carry out the computation at each node. To accomplish the objective of joint learning of all detectors, we propose a novel approach to combining classifier outputs during training that better matches the hard cascade setting in which the learned system will be deployed. This work is motivated by research in the area of mobile health where energy efficient real time detectors integrating information from multiple wireless on-body sensors and a smart phone are needed for real-time monitoring and the delivery of just-in-time adaptive interventions. We evaluate our framework on mobile sensor-based human activity recognition and mobile health detector learning problems.",
keywords = "Cascaded classification, low-power embedded sensing networks, mobile health",
pubstate = "forthcoming",
tppubtype = "inproceedings"
}

@article{boruvka2016assessing,
author = "Audrey Boruvka and Daniel Almirall and Katie Witkiewitz and Susan A Murphy",
title = "Assessing Time-Varying Causal Effect Moderation in Mobile Health",
journal = "arXiv preprint arXiv:1601.00237",
year = 2016,
abstract = "In mobile health interventions aimed at behavior change and maintenance, treatments are provided in real time to manage current or impending high risk situations or promote healthy behaviors in near real time. Currently there is great scientific interest in developing data analysis approaches to guide the development of mobile interventions. In particular data from mobile health studies might be used to examine effect moderators — individual characteristics, time-varying context or past treatment response that moderate the effect of current treatment on a subsequent response. This paper introduces a formal definition for moderated effects in terms of potential outcomes, a definition that is particularly suited to mobile interventions, where treatment occasions are numerous, individuals are not always available for treatment, and potential moderators might be influenced by past treatment. Methods for estimating moderated effects are developed and compared. The proposed approach is illustrated using BASICS-Mobile, a smartphone-based intervention designed to curb heavy drinking and smoking among college students.",
date = "2016-01-01",
pubstate = "published",
tppubtype = "article",
url = "http://arxiv.org/abs/1601.00237"
}

@inproceedings{adams2016hierarchical,
author = "Roy J Adams and Abinhav Parate and Benjamin M Marlin",
title = "Hierarchical Span-Based Conditional Random Fields for Labeling and Segmenting Events in Wearable Sensor Data Streams",
booktitle = "Proceedings of The 33rd International Conference on Machine Learning",
year = 2016,
pages = "334--343",
abstract = "The field of mobile health (mHealth) has the potential to yield new insights into health and behavior through the analysis of continuously recorded data from wearable health and activity sensors. In this paper, we present a hierarchi-cal span-based conditional random field model for the key problem of jointly detecting discrete events in such sensor data streams and segment-ing these events into high-level activity sessions. Our model includes higher-order cardinality fac-tors and inter-event duration factors to capture domain-specific structure in the label space. We show that our model supports exact MAP in-ference in quadratic time via dynamic program-ming, which we leverage to perform learning in the structured support vector machine frame-work. We apply the model to the problems of smoking and eating detection using four real data sets. Our results show statistically significant improvements in segmentation performance rel-ative to a hierarchical pairwise CRF.",
date = "2016-01-01",
pubstate = "published",
tppubtype = "inproceedings",
url = "http://www.jmlr.org/proceedings/papers/v48/adams16.pdf"
}

@article{7501575,
title = "Control Engineering Methods for the Design of Robust Behavioral Treatments",
author = "Korkut Bekiroglu and Constantino Lagoa and Susan A. Murphy and Stephanie T. Lanza",
url = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7501575",
doi = "10.1109/TCST.2016.2580661",
issn = "1063-6536",
year = 2016,
date = "2016-01-01",
journal = "IEEE Transactions on Control Systems Technology",
volume = "PP",
number = 99,
pages = "1-12",
abstract = "In this paper, a robust control approach is used to address the problem of adaptive behavioral treatment design. Human behavior (e.g., smoking and exercise) and reactions to treatment are complex and depend on many unmeasurable external stimuli, some of which are unknown. Thus, it is crucial to model human behavior over many subject responses. We propose a simple (low order) uncertain affine model subject to uncertainties whose response covers the most probable behavioral responses. The proposed model contains two different types of uncertainties: uncertainty of the dynamics and external perturbations that patients face in their daily life. Once the uncertain model is defined, we demonstrate how least absolute shrinkage and selection operator (lasso) can be used as an identification tool. The lasso algorithm provides a way to directly estimate a model subject to sparse perturbations. With this estimated model, a robust control algorithm is developed, where one relies on the special structure of the uncertainty to develop efficient optimization algorithms. This paper concludes by using the proposed algorithm in a numerical experiment that simulates treatment for the urge to smoke.",
keywords = "Adaptation models;Algorithm design and analysis;Design methodology;Optimization;Robust control;Robustness;Uncertainty;Adaptive treatment design;adaptive-robust intervention;behavioral treatment design;min-max structured robust optimization;receding horizon control.",
pubstate = "published",
tppubtype = "article"
}

@inproceedings{Sarker:2016:FSS:2858036.2858218,
author = "Sarker, Hillol and Tyburski, Matthew and Rahman, Md Mahbubur and Hovsepian, Karen and Sharmin, Moushumi and Epstein, David H. and Preston, Kenzie L. and Furr-Holden, C. Debra and Milam, Adam and Nahum-Shani, Inbal and al'Absi, Mustafa and Kumar, Santosh",
title = "Finding Significant Stress Episodes in a Discontinuous Time Series of Rapidly Varying Mobile Sensor Data",
booktitle = "Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems",
year = 2016,
series = "CHI '16",
pages = "4489--4501",
address = "Santa Clara, California, USA",
publisher = "ACM",
abstract = "Management of daily stress can be greatly improved by de- livering sensor-triggered just-in-time interventions (JITIs) on mobile devices. The success of such JITIs critically depends on being able to mine the time series of noisy sensor data to find the most opportune moments. In this paper, we propose a time series pattern mining method to detect significant stress episodes in a time series of discontinuous and rapidly varying stress data. We apply our model to 4 weeks of physiological, GPS, and activity data collected from 38 users in their natu-ral environment to discover patterns of stress in real-life. We find that the duration of a prior stress episode predicts the du-ration of the next stress episode and stress in mornings and evenings is lower than during the day. We then analyze the relationship between stress and objectively rated disorder in the surrounding neighborhood and develop a model to predict stressful episodes.",
date = "2016-01-01",
doi = "10.1145/2858036.2858218",
isbn = "978-1-4503-3362-7",
keywords = "intervention, mobile health (mHealth), stress management",
pubstate = "published",
tppubtype = "inproceedings",
url = "http://doi.acm.org/10.1145/2858036.2858218"
}

@inproceedings{Chatterjee2016Crave,
author = "Soujanya Chatterjee and Karen Hovsepian and Hillol Sarker and Nazir Saleheen and Mustafa al’Absi and Gowtham Atluri and Emre Ertin and Cho Lam and Andrine Lemieux and Motohiro Nakajima and Bonnie Spring and David W. Wetter and Santosh Kumar",
title = "mCrave: Continuous Estimation of Craving During Smoking Cessation",
booktitle = "Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing",
year = 2016,
pages = "863-874",
address = "New York, NY USA",
publisher = "ACM",
abstract = "Craving usually precedes a lapse for impulsive behaviors such as overeating, drinking, smoking, and drug use. Passive estimation of craving from sensor data in the natural environment can be used to assist users in coping with craving. In this paper, we take the first steps towards developing a computational model to estimate cigarette craving (during smoking abstinence) at the minute-level using mobile sensor data. We use 2,012 hours of sensor data and 1,812 craving self-reports from 61 participants in a smoking cessation study. To estimate craving, we first obtain a continuous measure of stress from sensor data. We find that during hours of day when craving is high, stress associated with self-reported high craving is greater than stress associated with low craving. We use this and other insights to develop feature functions, and encode them as pattern detectors in a Conditional Random Field (CRF) based model to infer craving probabilities.",
date = "2016-09-12",
doi = "10.1145/2971648.2971672",
isbn = "978-1-4503-4461-6",
keywords = "Craving, mobile health, smoking cessation, Stress",
pubstate = "published",
tppubtype = "inproceedings",
url = "http://doi.acm.org/10.1145/2971648.2971672"
}

@inproceedings{Abowd2016,
author = "Cheng Zhang and Junrui Yang and Caleb Southern and Thad Starner and Gregory Abowd",
title = "WatchOut: Extending Interactions on a Smartwatch with Inertial Sensing",
booktitle = "Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing",
year = 2016,
abstract = "Current interactions on a smartwatch are generally limited to a tiny touchscreen, physical buttons or knobs, and speech. We present WatchOut, a suite of interaction techniques that includes three families of tap and swipe gestures which extend input modalities to the watch's case, bezel, and band. We describe the implementation of a user-independent gesture recognition pipeline based on data from the watch's embedded inertial sensors. In a study with 12 participants using both a round- and square-screen watch, the average gesture classification accuracies ranged from 88.7% to 99.4%. We demonstrate applications of this richer interaction capability, and discuss the strengths, limitations, and future potential for this work.",
date = "2016-09-12",
keywords = "inertial sensing, interactions, smartwatch",
pubstate = "published",
tppubtype = "inproceedings",
url = "http://dl.acm.org/citation.cfm?id=2971775"
}

@inproceedings{Reyes2016,
author = "Gabriel Reyes and Dingtian Zhang and Sarthak Ghosh and Pratik Shah and Jason Wu and Aman Parnami and Bailey Bercik and Thad Starner and Gregory D. Abowd and W. Keith Edwards",
title = "Whoosh: Non-Voice Acoustics for Low-Cost, Hands-Free, and Rapid Input on Smartwatches",
booktitle = "Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (to appear)",
year = 2016,
abstract = "We present an alternate approach to smartwatch interactions using non-voice acoustic input captured by the device’s microphone to complement touch and speech. Whoosh is an interaction technique that recognizes the type and length of acoustic events performed by the user to enable low-cost, hands-free, and rapid input on smartwatches. We build a recognition system capable of detecting non-voice events directed at and around the watch, including blows, sip-and-puff, and directional air swipes, without hardware modifications to the device. Further, inspired by the design of musical instruments, we develop a custom modification of the physical structure of the watch case to passively alter the acoustic response of events around the bezel; this physical redesign expands our input vocabulary with no additional electronics. We evaluate our technique across 8 users with 10 events exhibiting up to 90.5% ten-fold cross validation accuracy on an unmodified watch, and 14 events with 91.3% ten-fold cross validation accuracy with an instrumental watch case. Finally, we share a number of demonstration applications, including multi-device interactions, to highlight our technique with a real-time recognizer running on the watch.",
date = "2016-09-12",
keywords = "hands-free, interaction techniques, Interfaces, non-voice acoustics, on-body input, smartwatches, wearable computing",
pubstate = "published",
tppubtype = "inproceedings",
url = "http://www.cc.gatech.edu/~keith//pubs/iswc2016-whoosh.pdf"
}

@inproceedings{Natarajan:2016:DAM:2971648.2971666b,
author = "Annamalai Natarajan and Gustavo Angarita and Edward Gaiser and Robert Malison and Deepak Ganesan and Benjamin M. Marlin",
title = "Domain Adaptation Methods for Improving Lab-to-field Generalization of Cocaine Detection Using Wearable ECG",
booktitle = "Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing",
year = 2016,
series = "UbiComp '16",
pages = "875--885",
address = "Heidelberg, Germany",
publisher = "ACM",
abstract = "Mobile health research on illicit drug use detection typically involves a two-stage study design where data to learn detectors is first collected in lab-based trials, followed by a deployment to subjects in a free-living environment to assess detector performance. While recent work has demonstrated the feasibility of wearable sensors for illicit drug use detection in the lab setting, several key problems can limit lab-to-field generalization performance. For example, lab-based data collection often has low ecological validity, the ground-truth event labels collected in the lab may not be available at the same level of temporal granularity in the field, and there can be significant variability between subjects. In this paper, we present domain adaptation methods for assessing and mitigating potential sources of performance loss in lab-to-field generalization and apply them to the problem of cocaine use detection from wearable electrocardiogram sensor data.",
date = "2016-09-12",
doi = "10.1145/2971648.2971666",
isbn = "978-1-4503-4461-6",
keywords = "classification, cocaine detection, covariate shift, domain adaptation, prior probability shift, Wearable Sensors",
pubstate = "published",
tppubtype = "inproceedings",
url = "http://doi.acm.org/10.1145/2971648.2971666"
}

@article{Nahum-Shani2016,
author = "Inbal Nahum-Shani and Shawna N. Smith and Bonnie J. Spring and Linda M. Collins and Katie Witkiewitz and Ambuj Tewari and Susan A. Murphy",
title = "Just-in-Time Adaptive Interventions (JITAIs) in Mobile Health: Key Components and Design Principles for Ongoing Health Behavior Support",
journal = "Annals of Behavioral Medicine",
year = 2016,
pages = "1--17",
issn = "1532-4796",
abstract = "Background. The just-in-time adaptive intervention (JITAI) is an intervention design aiming to provide the right type/amount of support, at the right time, by adapting to an individual’s changing internal and contextual state. The availability of increasingly powerful mobile and sensing technologies underpins the use of JITAIs to support health behavior, as in such a setting an individual’s state can change rapidly, unexpectedly, and in his/her natural environment. Purpose. Despite the increasing use and appeal of JITAIs, a major gap exists between the growing technological capabilities for delivering JITAIs and research on the development and evaluation of these interventions. Many JITAIs have been developed with minimal use of empirical evidence, theory, or accepted treatment guidelines. Here, we take an essential first step towards bridging this gap. Methods. Building on health behavior theories and the extant literature on JITAIs, we clarify the scientific motivation for JITAIs, define their fundamental components, and highlight design principles related to these components. Examples of JITAIs from various domains of health behavior research are used for illustration. Conclusion. As we enter a new era of technological capacity for delivering JITAIs, it is critical that researchers develop sophisticated and nuanced health behavior theories capable of guiding the construction of such interventions. Particular attention has to be given to better understanding the implica-tions of providing timely and ecologically sound support for intervention adherence and retention.",
date = "2016-09-23",
doi = "10.1007/s12160-016-9830-8",
pubstate = "published",
tppubtype = "article",
url = "http://dx.doi.org/10.1007/s12160-016-9830-8"
}

@article{yardley2016understanding,
author = "Lucy Yardley and Bonnie J Spring and Heleen Riper and Leanne G Morrisonand David H Crane and Kristina Curtis and Gina C Merchant and Felix Naughton and Ann Blandford",
title = "Understanding and promoting effective engagement with digital behavior change interventions",
journal = "American Journal of Preventive Medicine",
year = 2016,
volume = 51,
number = 5,
pages = "833--842",
abstract = "This paper is one in a series developed through a process of expert consensus to provide an overview of questions of current importance in research into engagement with digital behavior change interventions, identifying guidance based on research to date and priority topics for future research. The ﬁrst part of this paper critically reﬂects on current approaches to conceptualizing and measuring engagement. Next, issues relevant to promoting effective engagement are discussed, including how best to tailor to individual needs and combine digital and human support. A key conclusion with regard to conceptualizing engagement is that it is important to understand the relationship between engagement with the digital intervention and the desired behavior change. This paper argues that it may be more valuable to establish and promote “effective engagement,” rather than simply more engagement, with “effective engagement” deﬁned empirically as sufﬁcient engagement with the intervention to achieve intended outcomes. Appraisal of the value and limitations of methods of assessing different aspects of engagement highlights the need to identify valid and efﬁcient combinations of measures to develop and test multidimensional models of engagement. The ﬁnal section of the paper reﬂects on how interventions can be designed to ﬁt the user and their speciﬁc needs and context. Despite many unresolved questions posed by novel and rapidly changing technologies, there is widespread consensus that successful intervention design demands a user-centered and iterative approach to development, using mixed methods and in-depth qualitative research to progressively reﬁne the intervention to meet user requirements.",
date = "2016-10-13",
publisher = "Elsevier",
pubstate = "published",
tppubtype = "article"
}

@article{Patrick2016816,
title = "The Pace of Technologic Change: Implications for Digital Health Behavior Intervention Research",
author = "Kevin Patrick and Eric B. Hekler and Deborah Estrin and David C. Mohr and Heleen Riper and David Crane and Job Godino and William T. Riley",
url = "http://www.sciencedirect.com/science/article/pii/S0749379716301386",
doi = "http://dx.doi.org/10.1016/j.amepre.2016.05.001",
issn = "0749-3797",
year = 2016,
date = "2016-10-17",
journal = "American Journal of Preventive Medicine",
volume = 51,
number = 5,
pages = "816 - 824",
abstract = "This paper addresses the rapid pace of change in the technologies that support digital interventions; the complexity of the health problems they aim to address; and the adaptation of scientific methods to accommodate the volume, velocity, and variety of data and interventions possible from these technologies. Information, communication, and computing technologies are now part of every societal domain and support essentially every facet of human activity. Ubiquitous computing, a vision articulated fewer than 30 years ago, has now arrived. Simultaneously, there is a global crisis in health through the combination of lifestyle and age-related chronic disease and multiple comorbidities. Computationally intensive health behavior interventions may be one of the most powerful methods to reduce the consequences of this crisis, but new methods are needed for health research and practice, and evidence is needed to support their widespread use. The challenges are many, including a reluctance to abandon timeworn theories and models of health behaviorâ€”and health interventions more broadlyâ€”that emerged in an era of self-reported data; medical models of prevention, diagnosis, and treatment; and scientific methods grounded in sparse and expensive data. There are also many challenges inherent in demonstrating that newer approaches are, indeed, effective. Potential solutions may be found in leveraging methods of research that have been shown to be successful in other domains, particularly engineering. A more â€œagile scienceâ€� may be needed that streamlines the methods through which elements of health interventions are shown to work or not, and to more rapidly deploy and iteratively improve those that do. There is much to do to advance the issues discussed in this paper, and the papers in this theme issue. It remains an open question whether interventions based in these new models and methods are, in fact, equally if not more efficacious as what is available currently. Economic analyses of these new approaches are needed because assumptions of net worth compared to other approaches are just that, assumptions. Human-centered design research is needed to ensure that users ultimately benefit. Finally, a translational research agenda will be needed, as the status quo will likely be resistant to change.",
keywords = "",
pubstate = "published",
tppubtype = "article"
}

@inproceedings{sugavanam2016recovery,
author = "Nithin Sugavanam and Siddharth Baskar and Emre Ertin",
title = "Recovery guarantees for high resolution radar sensing with compressive illumination",
booktitle = "Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing (CoSeRa), 2016 4th International Workshop on",
year = 2016,
pages = "252--256",
organization = "IEEE",
abstract = "We present a compressive radar design that combines multitone linear frequency modulated (LFM) waveforms on transmit with classical stretch processor and sub-Nyquist sampling on receive. The proposed compressive illumination scheme has much fewer random elements compared to previously proposed compressive radar designs based on stochastic waveforms, resulting in reduced storage and complexity for implementation. We present bounds on the operator norm and mutual coherence of the sensing matrix of the proposed scheme and show that for sufficiently large number of modulating tones, high resolution range recovery is guaranteed for a sparse scene using sampling rates that scale linearly with the scene sparsity. Simulation results are presented to study recovery performance as a function of system parameters for targets both on and off the grid. In addition, we present experimental results using a high speed digital waveform generator and a custom designed analog stretch processor.",
date = "2016-11-17",
pubstate = "published",
tppubtype = "inproceedings"
}

@inproceedings{Chen:2015:GCR:2809695.2809711,
author = "Chen, Tiffany Yu-Han and Ravindranath, Lenin and Deng, Shuo and Bahl, Paramvir and Balakrishnan, Hari",
title = "Glimpse: Continuous, Real-Time Object Recognition on Mobile Devices",
booktitle = "Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems",
year = 2015,
series = "SenSys '15",
pages = "155--168",
address = "New York, NY, USA",
publisher = "ACM",
abstract = "Glimpse is a continuous, real-time object recognition system for camera-equipped mobile devices. Glimpse captures full-motion video, locates objects of interest, recognizes and labels them, and tracks them from frame to frame for the user. Because the algorithms for object recognition entail significant computation, Glimpse runs them on server machines. When the latency between the server and mobile device is higher than a frame-time, this approach lowers object recognition accuracy. To regain accuracy, Glimpse uses an active cache of video frames on the mobile device. A subset of the frames in the active cache are used to track objects on the mobile, using (stale) hints about objects that arrive from the server from time to time. To reduce network bandwidth usage, Glimpse computes trigger frames to send to the server for recognizing and labeling. Experiments with Android smartphones and Google Glass over Verizon, AT&T, and a campus Wi-Fi network show that with hardware face detection support (available on many mobile devices), Glimpse achieves precision between 96.4% to 99.8% for continuous face recognition, which improves over a scheme performing hardware face detection and server-side recognition without Glimpse's techniques by between 1.8-2.5×. The improvement in precision for face recognition without hardware detection is between 1.6-5.5×. For road sign recognition, which does not have a hardware detector, Glimpse achieves precision between 75% and 80%; without Glimpse, continuous detection is non-functional (0.2%-1.9% precision).",
acmid = 2809711,
doi = "10.1145/2809695.2809711",
isbn = "978-1-4503-3631-4",
keywords = "caching, cloud computing, google glass, mobile computing, wearable computing",
location = "Seoul, South Korea",
numpages = 14,
url = "http://doi.acm.org/10.1145/2809695.2809711"
}

@article{Kumar2015,
author = "Kumar, Santosh and Abowd, Gregory D and Abraham, William T and al’Absi, Mustafa and Gayle Beck, J and Chau, Duen Horng and Condie, Tyson and Conroy, David E and Ertin, Emre and Estrin, Deborah and Ganesan, Deepak and Lam, Cho and Marlin, Benjamin and Marsh, Clay B and Murphy, Susan A and Nahum-Shani, Inbal and Patrick, Kevin and Rehg, James M and Sharmin, Moushumi and Shetty, Vivek and Sim, Ida and Spring, Bonnie and Srivastava, Mani and Wetter, David W",
title = "Center of excellence for mobile sensor data-to-knowledge (MD2K)",
journal = "Journal of the American Medical Informatics Association",
year = 2015,
volume = 22,
number = 6,
pages = "1137-1142",
abstract = "Mobile sensor data-to-knowledge (MD2K) was chosen as one of 11 Big Data Centers of Excellence by the National Institutes of Health, as part of its Big Data-to-Knowledge initiative. MD2K is developing innovative tools to streamline the collection, integration, management, visualization, analysis, and interpretation of health data generated by mobile and wearable sensors. The goal of the big data solutions being developed by MD2K is to reliably quantify physical, biological, behavioral, social, and environmental factors that contribute to health and disease risk. The research conducted by MD2K is targeted at improving health through early detection of adverse health events and by facilitating prevention. MD2K will make its tools, software, and training materials widely available and will also organize workshops and seminars to encourage their use by researchers and clinicians.",
doi = "10.1093/jamia/ocv056",
eprint = "/oup/backfile/content_public/journal/jamia/22/6/10.1093_jamia_ocv056/2/ocv056.pdf",
url = "+ http://dx.doi.org/10.1093/jamia/ocv056"
}

@article{Liao2017,
author = "Peng Liao and Predrag Klasnja and Ambuj Tewari and Susan A. Murphy",
title = "Sample Size Calculations for Micro-randomized Trials in mHealth",
journal = "Statistics in Medicine",
year = 2015,
volume = 35,
number = 12,
pages = "1944-1971",
abstract = "The use and development of mobile interventions are experiencing rapid growth. In “just-in-time” mobile interventions, treatments are provided via a mobile device and they are intended to help an individual make healthy decisions “in the moment,” and thus have a proximal, near future impact. Currently the development of mobile interventions is proceeding at a much faster pace than that of associated data science methods. A first step toward developing data-based methods is to provide an experimental design for testing the proximal effects of these just-in-time treatments. In this paper, we propose a “micro-randomized” trial design for this purpose. In a micro-randomized trial, treatments are sequentially randomized throughout the conduct of the study, with the result that each participant may be randomized at the 100s or 1000s of occasions at which a treatment might be provided. Further, we develop a test statistic for assessing the proximal effect of a treatment as well as an associated sample size calculator. We conduct simulation evaluations of the sample size calculator in various settings. Rules of thumb that might be used in designing a micro-randomized trial are discussed. This work is motivated by our collaboration on the HeartSteps mobile application designed to increase physical activity.",
doi = "10.1002/sim.6847",
keywords = "mHealth, micro-randomized trial, Sample Size Calculation",
pubstate = "published",
tppubtype = "article",
url = "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4848174/pdf/nihms744437.pdf"
}

@article{2015arXiv150807964T,
author = "Teng, D. and Ertin, E.",
title = "Learning to Aggregate Information for Sequential Inferences",
journal = "ArXiv e-prints",
year = 2015,
abstract = "We consider the problem of training a binary sequential classiﬁer un-der an error rate constraint. It is well known that for known densities, accumulating the likelihood ratio statistics is time optimal under a ﬁxed error rate constraint. For the case of unknown densities, we formulate the learning for sequential detection problem as a constrained density ratio estimation problem. Speciﬁcally, we show that the problem can be posed as a convex optimization problem using a Reproducing Kernel Hilbert Space representation for the log-density ratio function. The proposed bi-nary sequential classiﬁer is tested on synthetic data set and UC Irvine human activity recognition data set, together with previous approaches for density ratio estimation. Our empirical results show that the classiﬁer trained through the proposed technique achieves smaller average sampling cost than previous classiﬁers proposed in the literature for the same error rate.",
date = "2015-01-01",
keywords = "Computer Science - Learning, Statistics - Machine Learning",
pubstate = "published",
tppubtype = "article"
}

Acar Tamersoy, Munmun De Choudhury and Duen Horng Chau. Characterizing Smoking and Drinking Abstinence from Social Media. In Proceedings of the 26th ACM Conference on Hypertext &#38; Social Media. 2015, 139–148. URL, DOIBibTeX

@article{raey,
title = "Building new computational models to support health behavior change and maintenance: new opportunities in behavioral research",
author = "Donna Spruijt-Metz and Eric Hekler and Niilo Saranummi and Stephen Intille and Ilkka Korhonen and Wendy Nilsen and Daniel E. Rivera and Bonnie Spring and Susan Michie and David A. Asch and Alberto Sanna and Vicente Traver Salcedo and Rita Kukakfa and Misha Pavel",
url = "http://dx.doi.org/10.1007/s13142-015-0324-1",
doi = "10.1007/s13142-015-0324-1",
issn = "1869-6716",
year = 2015,
date = "2015-01-01",
journal = "Translational Behavioral Medicine",
volume = 5,
number = 3,
pages = "335-346",
publisher = "Springer US",
abstract = "Adverse and suboptimal health behaviors and habits are responsible for approximately 40 % of preventable deaths, in addition to their unfavorable effects on quality of life and economics. Our current understanding of human behavior is largely based on static “snapshots” of human behavior, rather than ongoing, dynamic feedback loops of behavior in response to ever-changing biological, social, personal, and environmental states. This paper first discusses how new technologies (i.e., mobile sensors, smartphones, ubiquitous computing, and cloud-enabled processing/computing) and emerging systems modeling techniques enable the development of new, dynamic, and empirical models of human behavior that could facilitate just-in-time adaptive, scalable interventions. The paper then describes concrete steps to the creation of robust dynamic mathematical models of behavior including: (1) establishing “gold standard” measures, (2) the creation of a behavioral ontology for shared language and understanding tools that both enable dynamic theorizing across disciplines, (3) the development of data sharing resources, and (4) facilitating improved sharing of mathematical models and tools to support rapid aggregation of the models. We conclude with the discussion of what might be incorporated into a “knowledge commons,” which could help to bring together these disparate activities into a unified system and structure for organizing knowledge about behavior.",
keywords = "Mobile health; mHealth; Connected health; Health-related behavior; Just-in-time adaptive interventions; Real-time interventions; Computational models of behavior",
pubstate = "published",
tppubtype = "article"
}

@inproceedings{Hovsepian:2015:CTG:2750858.2807526,
author = "Hovsepian, Karen and al'Absi, Mustafa and Ertin, Emre and Kamarck, Thomas and Nakajima, Motohiro and Kumar, Santosh",
title = "cStress: Towards a Gold Standard for Continuous Stress Assessment in the Mobile Environment",
booktitle = "Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing",
year = 2015,
series = "UbiComp '15",
pages = "493--504",
address = "Osaka, Japan",
publisher = "ACM",
abstract = "Recent advances in mobile health have produced several new models for inferring stress from wearable sensors. But, the lack of a gold standard is a major hurdle in making clinical use of continuous stress measurements derived from wear-able sensors. In this paper, we present a stress model (called cStress) that has been carefully developed with attention to every step of computational modeling including data collec-tion, screening, cleaning, filtering, feature computation, nor-malization, and model training. More importantly, cStress was trained using data collected from a rigorous lab study with 21 participants and validated on two independently col-lected data sets — in a lab study on 26 participants and in a week-long field study with 20 participants. In testing, the model obtains a recall of 89% and a false positive rate of 5%on lab data. On field data, the model is able to predict each instantaneous self-report with an accuracy of 72%.",
date = "2015-01-01",
doi = "10.1145/2750858.2807526",
isbn = "978-1-4503-3574-4",
keywords = "mobile health (mHealth), modeling, Stress, Wearable Sensors",
pubstate = "published",
tppubtype = "inproceedings",
url = "http://doi.acm.org/10.1145/2750858.2807526"
}

@article{Amat-Santos2015,
title = "Left atrial decompression through unidirectional left-to-right interatrial shunt for the treatment of left heart failure: first-in-man experience with the V-Wave device.",
author = "I.J. Amat-Santos and S. Bergeron and M. Bernier and R. Allende and H.B. Ribeiro and M. Urena and P. Pibarot and S. Verheye and G. Keren and M. Yaacoby and Y. Nitzan and W.T. Abraham and J. Rodés-Cabau",
url = "http://www.pcronline.com/eurointervention/ahead_of_print/201405-07/",
year = 2015,
date = "2015-01-01",
journal = "EuroIntervention",
volume = 10,
number = 9,
pages = "1127--1131",
institution = "Lung Institute, Quebec City, Quebec, Canada.",
abstract = "Elevated filling pressures of the left atrium (LA) are associated with poorer outcomes in patients with chronic heart failure. The V-Wave is a new percutaneously implanted device intended to decrease the LA pressure by the shunting of blood from the LA to the right atrium. This report describes the first-in-man experience with the V-Wave device.A 70-year-old man with a history of heart failure of ischaemic origin, left ventricular dysfunction (LVEF: 35%, pulmonary wedge: 19 mmHg), no right heart dysfunction, NYHA Class III and orthopnoea despite optimal treatment, was accepted for V-Wave device implantation. The device consists of an ePTFE encapsulated nitinol frame that is implanted at the level of the interatrial septum and contains a trileaflet pericardium tissue valve sutured inside which allows a unidirectional LA to right atrium shunt. The procedure was performed through a transfemoral venous approach under fluoroscopic and TEE guidance. The device was successfully implanted and the patient was discharged 24 hours after the procedure with no complications. At three-month follow-up a left-to-right shunt through the device was confirmed by TEE. The patient was in NYHA Class II, without orthopnoea, the Kansas City Cardiomyopathy index was 77.6 (from 39.1 at baseline) and NT-proBNP was 322 ng/mL (from 502 ng/mL at baseline). The QP/QS was 1.17 and the pulmonary wedge was 8 mmHg, with no changes in pulmonary pressure or right ventricular function.Left atrial decompression through a unidirectional left-to-right interatrial shunt represents a new concept for the treatment of patients with left ventricular failure. The present report shows the feasibility of applying this new therapy with the successful and uneventful implantation of the V-Wave device, which was associated with significant improvement in functional, quality of life and haemodynamic parameters at 90 days.",
keywords = "Heart failure, Left-to-right shunt, V-Wave",
pubstate = "published",
tppubtype = "article"
}

@article{Al-Bakri2015,
title = "Opportunistic insights into occupational health hazards associated with waterpipe tobacco smoking premises in the United kingdom.",
author = "A. Al-Bakri and M. Jawad and P. Salameh and M. al'Absi and S. Kassim",
url = "http://www.ncbi.nlm.nih.gov/pubmed/25684497",
year = 2015,
date = "2015-01-01",
journal = "Asian Pac J Cancer Prev",
volume = 16,
number = 2,
pages = "621--626",
institution = "Queen Mary, University of London, Barts and The London School of Medicine and Dentistry, Institute of Dentistry, London, UK E-mail : s.kassim@qmul.ac.uk.",
abstract = "Smokefree laws aim to protect employees and the public from the dangers of secondhand smoke. Waterpipe premises have significantly increased in number in the last decade, with anecdotal reports of poor compliance with the smokefree law. The literature is bereft of information pertaining to waterpipe premise employees. This study aimed to opportunistically gather knowledge about the occupational health hazards associated with working in waterpipe premises in London, England.Employees from seven convenience-sampled, smokefree-compliant waterpipe premises in London were observed for occupational activities. Opportunistic carbon monoxide (CO) measurements were made among those with whom a rapport had developed. Observations were thematically coded and analysed.Occupational hazards mainly included environmental smoke exposure. Waterpipe-serving employees were required to draw several puffs soon after igniting the coals, thereby providing quality assurance of the product. Median CO levels were 27.5ppm (range 21-55ppm) among these employees. Self-reported employee health was poor, with some suggestion that working patterns and smoke exposure was a contributory factor.The smokefree law in England does not appear to protect waterpipe premise employees from high levels of CO. Continued concerns surrounding chronic smoke exposure may contribute to poor self-reported physical and mental wellbeing.",
keywords = "carbon monoxide, health policy, privileged access interviewers, smoking, United Kingdom, Waterpipe",
pubstate = "published",
tppubtype = "article"
}

@article{Abraham201516,
title = "A Randomized Controlled Trial to Evaluate the Safety and Efficacy of Cardiac Contractility Modulation in Patients With Moderately Reduced Left Ventricular Ejection Fraction and a Narrow QRS Duration: Study Rationale and Design",
author = "W. T. Abraham and J. Lindenfeld and V.Y. Reddy and G. Hasenfuss and K. Kuck and J. Boscardin and R. Gibbons and D. Burkhoff",
url = "http://www.sciencedirect.com/science/article/pii/S1071916414012214",
issn = "1071-9164",
year = 2015,
date = "2015-01-01",
journal = "Journal of Cardiac Failure",
volume = 21,
number = 1,
pages = "16 - 23",
abstract = "Abstract Cardiac contractility modulation (CCM) signals are nonexcitatory electrical signals delivered during the cardiac absolute refractory period that enhance the strength of cardiac muscular contraction. The FIX-HF-5 study was a prospective randomized study comparing CCM plus optimal medical therapy (OMT) to OMT alone that included 428 New York Heart Association (NYHA) functional class III or IV heart failure patients with ejection fraction (EF) â¤45% according to core laboratory assessment. The study met its primary safety end point, but did not reach its primary efficacy end point: a responders analysis of changes in ventilatory anaerobic threshold (VAT). However, in a prespecified subgroup analysis, significant improvements in primary and secondary end points, including the responder VAT end point, were observed in patients with EFs ranging from 25% to 45%, who constituted about one-half of the study subjects. We therefore designed a new study to prospectively confirm the efficacy of CCM in this population. A hierarchic bayesian statistical analysis plan was developed to take advantage of the data already available from the first study. In addition, based on technical difficulties encountered in reliably quantifying VAT and the relatively large amount of nonquantifiable studies, the primary efficacy end point was changed to peak VO2, with significant measures incorporated to minimize the influence of placebo effect. In this paper, we provide the details and rationale of the FIX-HF-5C study design to study CCM plus OMT compared with OMT alone in subjects with normal QRS duration, NYHA functional class III or IV, and EF 25% to 45%. This study is registered on www.clinicaltrials.gov with identifier no. NCT01381172.",
keywords = "cardiac resynchronization therapy, cardiopulmonary stress testing, Heart failure, quality of life",
pubstate = "published",
tppubtype = "article"
}

@article{Costanzo2015,
title = "Mechanisms and Clinical Consequences of~Untreated Central Sleep Apnea in Heart~Failure.",
author = "M.R. Costanzo and R. Khayat and P. Ponikowski and R. Augostini and C. Stellbrink and M. Mianulli and W.T. Abraham",
url = "http://dx.doi.org/10.1016/j.jacc.2014.10.025",
year = 2015,
date = "2015-01-06",
journal = "Journal of the American College of Cardiology",
volume = 65,
number = 1,
pages = "72--84",
institution = "Division of Cardiovascular Medicine, The Ohio State University, Columbus, Ohio.",
abstract = "Central sleep apnea (CSA) is a highly prevalent, though often unrecognized, comorbidity in patients with heart failure (HF). Data from HF population studies suggest that it may present in 30% to 50% of HF patients. CSA is recognized as an important contributor to the progression of HF and to HF-related morbidity and mortality. Over the past 2 decades, an expanding body of research has begun to shed light on the pathophysiologic mechanisms of CSA. Armed with this growing knowledge base, the sleep, respiratory, and cardiovascular research communities have been working to identify ways to treat CSA in HF with the ultimate goal of improving patient quality of life and clinical outcomes. In this paper, we examine the current state of knowledge about the mechanisms of CSA in HF and review emerging therapies for this disorder.",
keywords = "apnea-hypopnea index, continuous positive airway pressure, hypoxia, reactive oxygen species, reoxygenation",
pubstate = "published",
tppubtype = "article"
}

@article{He2015,
title = "Visual aggregate analysis of eligibility features of clinical trials.",
author = "Z. He and S. Carini and I. Sim and C. Weng",
url = "http://dx.doi.org/10.1016/j.jbi.2015.01.005",
year = 2015,
date = "2015-01-20",
journal = "J Biomed Inform",
institution = "Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA. Electronic address: cw2384@cumc.columbia.edu.",
abstract = {To develop a method for profiling the collective populations targeted for recruitment by multiple clinical studies addressing the same medical condition using one eligibility feature each time.Using a previously published database COMPACT as the backend, we designed a scalable method for visual aggregate analysis of clinical trial eligibility features. This method consists of four modules for eligibility feature frequency analysis, query builder, distribution analysis, and visualization, respectively. This method is capable of analyzing (1) frequently used qualitative and quantitative features for recruiting subjects for a selected medical condition, (2) distribution of study enrollment on consecutive value points or value intervals of each quantitative feature, and (3) distribution of studies on the boundary values, permissible value ranges, and value range widths of each feature. All analysis results were visualized using Google Charts API. Five recruited potential users assessed the usefulness of this method for identifying common patterns in any selected eligibility feature for clinical trial participant selection.We implemented this method as a Web-based analytical system called VITTA (Visual Analysis Tool of Clinical Study Target Populations). We illustrated the functionality of VITTA using two sample queries involving quantitative features BMI and HbA1c for conditions "hypertension" and "Type 2 diabetes", respectively. The recruited potential users rated the user-perceived usefulness of VITTA with an average score of 86.4/100.We contributed a novel aggregate analysis method to enable the interrogation of common patterns in quantitative eligibility criteria and the collective target populations of multiple related clinical studies. A larger-scale study is warranted to formally assess the usefulness of VITTA among clinical investigators and sponsors in various therapeutic areas.},
keywords = "Clinical trial, Knowledge management, Patient selection, Selection bias",
pubstate = "published",
tppubtype = "article"
}

@article{Poncela-Casasnovas2015,
title = "Social embeddedness in an online weight management programme is linked to greater weight loss.",
author = "J. Poncela-Casasnovas and B. Spring and D. McClary and A.C. Moller and R. Mukogo and C.A. Pellegrini and M.J. Coons and M. Davidson and S. Mukherjee and L.A. Nunes Amaral",
url = "http://dx.doi.org/10.1098/rsif.2014.0686",
year = 2015,
date = "2015-01-28",
journal = "the Journal of the Royal Society Interface",
volume = 12,
number = 104,
institution = "Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL 60208, USA HHMI, Northwestern University, Evanston, IL 60208, USA Northwestern Institute on Complex Systems, No",
abstract = "The obesity epidemic is heightening chronic disease risk globally. Online weight management (OWM) communities could potentially promote weight loss among large numbers of people at low cost. Because little is known about the impact of these online communities, we examined the relationship between individual and social network variables, and weight loss in a large, international OWM programme. We studied the online activity and weight change of 22 419 members of an OWM system during a six-month period, focusing especially on the 2033 members with at least one friend within the community. Using Heckman's sample-selection procedure to account for potential selection bias and data censoring, we found that initial body mass index, adherence to self-monitoring and social networking were significantly correlated with weight loss. Remarkably, greater embeddedness in the network was the variable with the highest statistical significance in our model for weight loss. Average per cent weight loss at six months increased in a graded manner from 4.1% for non-networked members, to 5.2% for those with a few (two to nine) friends, to 6.8% for those connected to the giant component of the network, to 8.3% for those with high social embeddedness. Social networking within an OWM community, and particularly when highly embedded, may offer a potent, scalable way to curb the obesity epidemic and other disorders that could benefit from behavioural changes.",
keywords = "complex networks, modelling, obesity, Weight loss",
pubstate = "published",
tppubtype = "article"
}

A M Lemieux, B Li and M al'Absi. Khat use and appetite: an overview and comparison of amphetamine, khat and cathinone.. Journal of Ethnopharmacology 160:78–85, 2015. URLBibTeX

@article{Lemieux2015,
title = "Khat use and appetite: an overview and comparison of amphetamine, khat and cathinone.",
author = "A.M. Lemieux and B. Li and M. al'Absi",
url = "http://dx.doi.org/10.1016/j.jep.2014.11.002",
year = 2015,
date = "2015-02-03",
journal = "Journal of Ethnopharmacology",
volume = 160,
pages = "78--85",
institution = "University of Minnesota Medical School Duluth Campus, Duluth, MN, USA. Electronic address: malabsi@umn.edu.",
abstract = "To understand the role of khat (Catha edulis) use on the aberrations in appetite and weight which are common comorbidities for khat and other amphetamine users.We provide a comprehensive overview and conceptual summary of the historical cultural use of khat as a natural stimulant and describe the similarities and differences between cathinone (the main psychoactive constituent of khat) and amphetamine highlighting the limited literature on the neurophysiology of appetite and subsequent weight effects of khat.Animal and some human studies indicate that khat produces appetite suppression, although little is known about mechanisms of this effect. Both direct and indirect effects of khat stem from multiple factors including behavioral, chemical and neurophysiological effects on appetite and metabolism. Classic and newly identified appetite hormones have not been explored sufficiently in the study of appetite and khat use. Unique methodological challenges and opportunities are encountered when examining effects of khat and cathinone including khat-specific medical comorbidities, unique route of administration, differential patterns of behavioral effects relative to amphetamines and the nascent state of our understanding of the neurobiology of this drug.A considerable amount of work remains in the study of the appetite effects of khat chewing and outline a program of research that could inform our understanding of this natural amphetamine?s appetite effects and help prepare health care workers for the unique health effects of this drug.",
keywords = "Amphetamine, Appetite, Cathinone, Health, Khat, Weight loss",
pubstate = "published",
tppubtype = "article"
}

@article{rohtua,
title = "Evaluation of an electronic health record-supported obesity management protocol implemented in a community health center: a cautionary note",
author = "J. Steglitz and M. Sommers and M.R. Talen and L.K. Thornton and B. Spring",
url = "http://dx.doi.org/10.1093/jamia/ocu034",
issn = "1067-5027",
year = 2015,
date = "2015-02-09",
journal = "Journal of the American Medical Informatics Association",
publisher = "The Oxford University Press",
abstract = "Jeremy Steglitz1, Mary Sommers2, Mary R Talen2, Louise K Thornton1,3 and Bonnie Spring11Department of Preventive Medicine, Northwestern University, 680 N Lake Shore Dr, Chicago, IL 60611, USA2Erie Family Health Center, Chicago, IL, USA3Priority Research Centre for Translational Neuroscience and Mental Health, University of Newcastle, AustraliaCorrespondence to Jeremy Steglitz, MPH, MS, Email: jeremy.steglitzatnorthwestern.edu, Tel: 312.503.1216Received August 6, 2014.Revision received October 31, 2014.Accepted November 24, 2014.Abstract Objective Primary care clinicians are well-positioned to intervene in the obesity epidemic. We studied whether implementation of an obesity intake protocol and electronic health record (EHR) form to guide behavior modification would facilitate identification and management of adult obesity in a Federally Qualified Health Center serving low-income, Hispanic patients. Materials and Methods In three studies, we examined clinician and patient outcomes before and after the addition of the weight management protocol and form. In the Clinician Study, 12 clinicians self-reported obesity management practices. In the Population Study, BMI and order data from 5000 patients and all 40 clinicians in the practice were extracted from the EHR preintervention and postintervention. In the Exposure Study, EHR-documented outcomes for a sub-sample of 46 patients actually exposed to the obesity management form were compared to matched controls. Results Clinicians reported that the intake protocol and form increased their performance of obesity-related assessments and their confidence in managing obesity. However, no improvement in obesity management practices or patient weight-loss was evident in EHR records for the overall clinic population. Further analysis revealed that only 55 patients were exposed to the form. Exposed patients were twice as likely to receive weight-loss counseling following the intervention, as compared to before, and more likely than matched controls. However, their obesity outcomes did not differ. Conclusion Results suggest that an obesity intake protocol and EHR-based weight management form may facilitate clinician weight-loss counseling among those exposed to the form. Significant implementation barriers can limit exposure, however, and need to be addressed. electronic health recordobesityprimary careevidence-based practicecommunity healthtextcopyright The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissionsatoup.com For numbered affiliations see end of article.",
keywords = "community health, electronic health record, evidence-based practice, obesity, primary care",
pubstate = "published",
tppubtype = "article"
}

@techreport{nahumshani2014b,
title = "Just-in-time adaptive interventions (JITAIs): An organizing framework for ongoing health behavior support",
author = "I. Nahum-Shani and S.N. Smith and A. Tewari and K. Witkiewitz and L.M. Collins and B. Spring and S.A. Murphy",
url = "http://methodology.psu.edu/media/techreports/14-126.pdf",
year = 2015,
date = "2015-02-16",
journal = "Technical Report No. 14-126 The Methodology Center, Penn State",
volume = 14,
number = 126,
institution = "The Methodology Center, Pennsylvania State University",
abstract = "An emerging mobile phone intervention design, called the just-in-time adaptive intervention (JITAI), holds enormous potential for adapting mobile phone-delivered interventions to the dynamics of an individuals emotional, social, physical and contextual state, so as to prevent negative health outcomes and promote the adoption and maintenance of healthy behaviors. A JITAI is an intervention design aiming to address the dynamically changing needs of individuals via the provision of the type/amount of support needed, at the right time, and only when needed. Despite the increasing use and the practical and conceptual appeal of JITAIs, a comprehensive organizing scientific framework to guide the construction of efficacious evidence-based JITAIs has yet to be provided. To bridge this gap, the current manuscript provides an organizing scientific framework to guide the construction of JITAIs. The key components of a JITAI are described and illustrated using examples of JITAIs from various domains of psychological and health behavior research. Design principles are discussed, as well as practical and theoretical challenges that require consideration in the process of constructing efficacious JITAIs.",
keywords = "health behavior, Just-in-time adaptive interventions, mobile devices, support",
pubstate = "published",
tppubtype = "techreport"
}

@article{Bayram2015,
title = "d-Ribose aids heart failure patients with preserved ejection fraction and diastolic dysfunction: a pilot study.",
author = "M. Bayram and J.A. St. Cyr and W.T. Abraham",
url = "http://dx.doi.org/10.1177/1753944715572752",
year = 2015,
date = "2015-02-19",
journal = "Ther Adv Cardiovasc Dis",
institution = "Cardiovascular Medicine, Ohio State University, Columbus, OH, USA.",
abstract = "The incidence of heart failure continues to escalate with >550,000 newly diagnosed patients annually worldwide. More than half of the patients with heart failure have preserved ejection fraction or isolated diastolic dysfunction, for which no current effective therapies for diastolic dysfunction exist. Every cell requires adequate levels of high energy phosphates to maintain integrity and function. Previous studies have demonstrated that diastolic function is energy dependent and supplemental d-ribose has shown to improve diastolic dysfunction. This study investigated what role d-ribose might play in congestive heart failure patients with preserved systolic function and diastolic dysfunction.A total of 11 patients, New York Heart Association class II-IV, with clinical symptoms, normal left ventricular systolic function and echocardiographic evidence of diastolic dysfunction were enrolled after meeting inclusion criteria. Each patient received oral d-ribose (5 g/dose) for 6 weeks. Echocardiographic evaluation, cardiopulmonary metabolic testing and subjective questionnaire assessment were performed at baseline, 6 weeks and at 9 weeks (3 weeks after discontinuing d-ribose).An improvement in their tissue Doppler velocity (E'), which was maintained at 9 weeks, was demonstrated in 64% of the patients. Five patients showed an improvement in their ratio of early diastolic filling velocity (E) to early annulus relaxation velocity (E'). There was no appreciable difference in these measurements during valsalva or with leg raising and handgrip exercises. Four patients also had an improvement in their maximum predicted VO2 values; two demonstrated a worsening effect and no differences were noted in the remaining patients. Subjective assessment revealed a benefit in only one patient, worsening symptoms in one patient and no change in the remaining cohort.This pilot study revealed some beneficial trends with D-ribose even with this small cohort size. However, future investigations are necessary to further substantiate these observed benefits.",
keywords = "D-ribose, diastolic dysfunction, Heart failure, preserved systolic function",
pubstate = "published",
tppubtype = "article"
}

@article{Han2015,
title = "A Pilot Randomized Trial of Text-Messaging for Symptom Awareness and Diabetes Knowledge in Adolescents With Type 1 Diabetes",
author = "Y. Han and M.S. Faulkner and H. Fritz and D. Fadoju and A. Muir and G.D. Abowd and L. Head and R.I. Arriaga",
url = "http://www.sciencedirect.com/science/article/pii/S0882596315000342",
issn = "0882-5963",
year = 2015,
date = "2015-02-23",
journal = "Journal of Pediatric Nursing",
number = 0,
pages = "-",
abstract = "Adolescents with type 1 diabetes typically receive clinical care every 3 months. Between visits, diabetes-related issues may not be frequently reflected, learned, and documented by the patients, limiting their self-awareness and knowledge about their condition. We designed a text-messaging system to help resolve this problem. In a pilot, randomized controlled trial with 30 adolescents, we examined the effect of text messages about symptom awareness and diabetes knowledge on glucose control and quality of life. The intervention group that received more text messages between visits had significant improvements in quality of life.",
keywords = "Adolescents, Text messaging, Type 1 diabetes",
pubstate = "published",
tppubtype = "article"
}

@article{Wilson2015,
title = "When it comes to lifestyle recommendations, more is sometimes less",
author = "Kristina Wilson and Ibrahim Senay and Marta Durantini and Flor Sánchez and Michael Hennessyand Bonnie Spring and Doloroes Albarracín",
doi = "http://dx.doi.org/10.1037/a0038295",
year = 2015,
date = "2015-03-01",
journal = "Psychological Bulletin",
volume = 141,
number = 2,
pages = "474-509",
abstract = "A meta-analysis of 150 research reports summarizing the results of multiple behavior domain interventions examined theoretical predictions about the effects of the included number of recommendations on behavioral and clinical change in the domains of smoking, diet, and physical activity. The meta-analysis yielded 3 main conclusions. First, there is a curvilinear relation between the number of behavioral recommendations and improvements in behavioral and clinical measures, with a moderate number of recommendations producing the highest level of change. A moderate number of recommendations is likely to be associated with stronger effects because the intervention ensures the necessary level of motivation to implement the recommended changes, thereby increasing compliance with the goals set by the intervention, without making the intervention excessively demanding. Second, this curve was more pronounced when samples were likely to have low motivation to change, such as when interventions were delivered to nonpatient (vs. patient) populations, were implemented in nonclinic (vs. clinic) settings, used lay community (vs. expert) facilitators, and involved group (vs. individual) delivery formats. Finally, change in behavioral outcomes mediated the effects of number of recommended behaviors on clinical change. These findings provide important insights that can help guide the design of effective multiple behavior domain interventions. (PsycINFO Database Record (c) 2015 APA, all rights reserved)",
keywords = "behavior, JITAI, lifestyle",
pubstate = "published",
tppubtype = "article"
}

@article{Spring2015,
title = "Fostering multiple healthy lifestyle behaviors for primary prevention of cancer",
author = "Bonnie Spring and Abby C. King and Sherry L. Pagoto and Linda Van Horn and Jeffery D. Fisher",
editor = ". , http://dx.doi.org/10.1037/a0038806",
url = "https://www.ncbi.nlm.nih.gov/pubmed/25730716",
doi = "http://psycnet.apa.org/doi/10.1037/a0038806",
year = 2015,
date = "2015-03-01",
journal = "American Psychologist",
volume = 70,
number = 2,
pages = "75-90",
abstract = "The odds of developing cancer are increased by specific lifestyle behaviors (tobacco use, excess energy and alcohol intakes, low fruit and vegetable intake, physical inactivity, risky sexual behaviors, and inadequate sun protection) that are established risk factors for developing cancer. These behaviors are largely absent in childhood, emerge and tend to cluster over the life span, and show an increased prevalence among those disadvantaged by low education, low income, or minority status. Even though these risk behaviors are modifiable, few are diminishing in the population over time. We review the prevalence and population distribution of these behaviors and apply an ecological model to describe effective or promising healthy lifestyle interventions targeted to the individual, the sociocultural context, or environmental and policy influences. We suggest that implementing multiple health behavior change interventions across these levels could substantially reduce the prevalence of cancer and the burden it places on the public and the health care system. We note important still-unresolved questions about which behaviors can be intervened upon simultaneously in order to maximize positive behavioral synergies, minimize negative ones, and effectively engage underserved populations. We conclude that interprofessional collaboration is needed to appropriately determine and convey the value of primary prevention of cancer and other chronic diseases. (PsycINFO Database Record (c) 2015 APA, all rights reserved)",
keywords = ": cancer prevention, ecological model, health behavior, obesity, risk behavior, smoking",
pubstate = "published",
tppubtype = "article"
}

@article{Sharma2015129,
title = "Stratifying patients at the risk of heart failure hospitalization using existing device diagnostic thresholds",
author = "V. Sharma and L.D. Rathman and R.S. Small and D.J. Whellan and J. Koehler and E. Warman and W.T. Abraham",
url = "http://www.sciencedirect.com/science/article/pii/S014795631400418X",
issn = "0147-9563",
year = 2015,
date = "2015-03-01",
journal = "Heart & Lung: The Journal of Acute and Critical Care",
volume = 44,
number = 2,
pages = "129 - 136",
abstract = "AbstractBackground Heart failure hospitalizations (HFHs) cost the US health care system â¼$20 billion annually. Identifying patients at risk of HFH to enable timely intervention and prevent expensive hospitalization remains a challenge. Implantable cardioverter defibrillators (ICDs) and cardiac resynchronization devices with defibrillation capability (CRT-Ds) collect a host of diagnostic parameters that change with HF status and collectively have the potential to signal an increasing risk of HFH. These device-collected diagnostic parameters include activity, day and night heart rate, atrial tachycardia/atrial fibrillation (AT/AF) burden, mean rate during AT/AF, percent CRT pacing, number of shocks, and intrathoracic impedance. There are thresholds for these parameters that when crossed trigger a notification, referred to as device observation, which gets noted on the device report. We investigated if these existing device observations can stratify patients at varying risk of HFH. Methods We analyzed data from 775 patients (age: 69Â Â±Â 11 year, 68% male) with CRT-D devices followed for 13Â Â±Â 5 months with adjudicated HFHs. HFH rate was computed for increasing number of device observations. Data were analyzed by both excluding and including intrathoracic impedance. HFH risk was assessed at the time of a device interrogation session, and all the data between previous and current follow-up sessions were used to determine the HFH risk for the next 30 days. Results 2276 follow-up sessions in 775 patients were evaluated with 42 HFHs in 37 patients. Percentage of evaluations that were followed by an HFH within the next 30 days increased with increasing number of device observations. Patients with 3 or more device observations were at 42Ã HFH risk compared to patients with no device observation. Even after excluding intrathoracic impedance, the remaining device parameters effectively stratified patients at HFH risk. Conclusion Available device observations could provide an effective method to stratify patients at varying risk of heart failure hospitalization.",
keywords = "Implantable device diagnostics",
pubstate = "published",
tppubtype = "article"
}

@article{Rebar2015,
title = "Using the EZ-Diffusion Model to Score a Single-Category Implicit Association Test of Physical Activity.",
author = "A.L. Rebar and N. Ram and D.E. Conroy",
url = "http://dx.doi.org/10.1016/j.psychsport.2014.09.008",
year = 2015,
date = "2015-03-01",
journal = "Psychology of Sport & Exercise",
volume = 16,
number = 3,
pages = "96--105",
institution = "The Pennsylvania State University, Department of Kinesiology ; The Pennsylvania State University, Department of Human Development and Family Studies.",
abstract = "The Single-Category Implicit Association Test (SC-IAT) has been used as a method for assessing automatic evaluations of physical activity, but measurement artifact or consciously-held attitudes could be confounding the outcome scores of these measures. The objective of these two studies was to address these measurement concerns by testing the validity of a novel SC-IAT scoring technique.Study 1 was a cross-sectional study, and study 2 was a prospective study.In study 1, undergraduate students (N = 104) completed SC-IATs for physical activity, flowers, and sedentary behavior. In study 2, undergraduate students (N = 91) completed a SC-IAT for physical activity, self-reported affective and instrumental attitudes toward physical activity, physical activity intentions, and wore an accelerometer for two weeks. The EZ-diffusion model was used to decompose the SC-IAT into three process component scores including the information processing efficiency score.In study 1, a series of structural equation model comparisons revealed that the information processing score did not share variability across distinct SC-IATs, suggesting it does not represent systematic measurement artifact. In study 2, the information processing efficiency score was shown to be unrelated to self-reported affective and instrumental attitudes toward physical activity, and positively related to physical activity behavior, above and beyond the traditional D-score of the SC-IAT.The information processing efficiency score is a valid measure of automatic evaluations of physical activity.",
keywords = "Automatic evaluations, Exercise, Implicit attitudes, Response time measures",
pubstate = "published",
tppubtype = "article"
}

@article{Gold2015,
title = "The effect of reverse remodeling on long-term survival in mildly symptomatic patients with heart failure receiving cardiac resynchronization therapy: Results of the REVERSE study.",
author = "M.R. Gold and C. Daubert and W.T. Abraham and S. Ghio and M.S.J. Sutton and J.H. Hudnall and J. Cerkvenik and C. Linde",
url = "http://dx.doi.org/10.1016/j.hrthm.2014.11.014",
year = 2015,
date = "2015-03-01",
journal = "Heart Rhythm",
volume = 12,
number = 3,
pages = "524--530",
institution = "Karolinska University Hospital, Stockholm, Sweden.",
abstract = "Cardiac resynchronization therapy (CRT) reduces mortality, improves functional status, and induces reverse left ventricular remodeling in selected populations with heart failure (HF). The magnitude of reverse remodeling predicts survival with many HF medical therapies. However, there are few studies assessing the effect of remodeling on long-term survival with CRT.The purpose of this study was to assess the effect of CRT-induced reverse remodeling on long-term survival in patients with mildly symptomatic heart failure.The REsynchronization reVErses Remodeling in Systolic Left vEntricular Dysfunction trial was a multicenter, double-blind, randomized trial of CRT in patients with mild HF. Long-term follow-up of 5 years was preplanned. The present analysis was restricted to the 353 patients who were randomized to the CRT ON group with paired echocardiographic studies at baseline and 6 months postimplantation. The left ventricular end-systolic volume index (LVESVi) was measured in the core laboratory and was an independently powered end point of the REsynchronization reVErses Remodeling in Systolic Left vEntricular Dysfunction trial.A 68% reduction in mortality was observed in patients with ?15% decrease in LVESVi compared to the rest of the patients (P = .0004). Multivariable analysis showed that the change in LVESVi was a strong independent predictor (P = .0002), with a 14% reduction in mortality for every 10% decrease in LVESVi. Other remodeling parameters such as left ventricular end-diastolic volume index and ejection fraction had a similar association with mortality.The change in left ventricular end-systolic volume after 6 months of CRT is a strong independent predictor of long-term survival in mild HF.",
keywords = "cardiac resynchronization therapy, Defibrillator, Heart failure, Implantable cardioverter-defibrillator, Remodeling",
pubstate = "published",
tppubtype = "article"
}

@article{pienta2015,
title = "Interactive Querying over Large Network Data: Scalability, Visualization, and Interaction Design",
author = "R. Pienta and A. Tamersoy and H. Tong and A. Endert and D.H. Chau",
url = "http://www.cc.gatech.edu/~atamerso/papers/pienta_iui15.pdf",
year = 2015,
date = "2015-03-29",
journal = "Proceedings of the 20th ACM Conference on Intelligent User Interfaces (IUI '15)",
abstract = "Given the explosive growth of modern graph data, new methods are needed that allow for the querying of complex graph structures without the need of a complicated querying languages; in short, interactive graph querying is desirable. We describe our work towards achieving our overall research goal of designing and developing an interactive querying system for large network data. We focus on three critical aspects: scalable data mining algorithms, graph visualization, and interaction design. We have already completed an approximate subgraph matching system called MAGE in our previous work that fulfills the algorithmic foundation allowing us to query a graph with hundreds of millions of edges. Our preliminary work on visual graph querying, Graphite, was the first step in the process to making an interactive graph querying system. We are in the process of designing the graph visualization and robust interaction needed to make truly interactive graph querying a reality",
keywords = "Graph Querying and Mining; Visualization; Interaction Design",
pubstate = "published",
tppubtype = "article"
}

@article{Yang2015a,
title = "Implementation of Behavior Change Techniques in Mobile Applications for Physical Activity.",
author = "C. Yang and J.P. Maher and D.E. Conroy",
url = "http://dx.doi.org/10.1016/j.amepre.2014.10.010",
year = 2015,
date = "2015-04-01",
journal = "American Journal of Preventive Medicine",
institution = "Department of Preventive Medicine, Northwestern University, Chicago, Illinois. Electronic address: conroy@northwestern.edu.",
abstract = "Mobile applications (apps) for physical activity are popular and hold promise for promoting behavior change and reducing non-communicable disease risk. App marketing materials describe a limited number of behavior change techniques (BCTs), but apps may include unmarketed BCTs, which are important as well.To characterize the extent to which BCTs have been implemented in apps from a systematic user inspection of apps.Top-ranked physical activity apps (N=100) were identified in November 2013 and analyzed in 2014. BCTs were coded using a contemporary taxonomy following a user inspection of apps.Users identified an average of 6.6 BCTs per app and most BCTs in the taxonomy were not represented in any apps. The most common BCTs involved providing social support, information about others' approval, instructions on how to perform a behavior, demonstrations of the behavior, and feedback on the behavior. A latent class analysis of BCT configurations revealed that apps focused on providing support and feedback as well as support and education.Contemporary physical activity apps have implemented a limited number of BCTs and have favored BCTs with a modest evidence base over others with more established evidence of efficacy (e.g., social media integration for providing social support versus active self-monitoring by users). Social support is a ubiquitous feature of contemporary physical activity apps and differences between apps lie primarily in whether the limited BCTs provide education or feedback about physical activity.",
keywords = "BCT, behavior change techniques, measuring activity, mobile apps, taxonomy",
pubstate = "published",
tppubtype = "article"
}

@article{liao2015micro,
author = "P. Liao and P. Klasnja and A. Tewari and S.A. Murphy",
title = "Micro-Randomized Trials in mHealth",
journal = "Statistics in Medicine",
year = 2015,
abstract = "The use and development of mobile interventions is experiencing rapid growth. In “just-in-time” mobile interventions, treatments are provided via a mobile device that are intended to help an individual make healthy decisions “in the moment,” and thus have a proximal, near future impact. Currently the development of mobile interventions is proceeding at a much faster pace than that of associated data science methods. A first step toward developing data-based methods is to provide an experimental design for use in testing the proximal effects of these just-in-time treatments. In this paper, we propose a “micro-randomized” trial design for this purpose. In a micro-randomized trial, treatments are sequentially randomized throughout the conduct of the study, with the result that each participant may be randomized at the 100s or 1000s of occasions at which a treatment might be provided. Further, we develop a test statistic for assessing the proximal effect of a treatment as well as an associated sample size calculator. We conduct simulation evaluations of the sample size calculator in various settings. Rules of thumb that might be used in designing the micro-randomized trial are discussed. This work is motivated by our collaboration on the HeartSteps mobile application designed to increase physical activity.",
date = "2015-04-02",
keywords = "mHealth, Mirco-randomized Trial, Sample Size Calculation",
pubstate = "published",
tppubtype = "article",
url = "http://arxiv.org/pdf/1504.00238.pdf"
}

@article{Kennedy2015,
title = "Continuous In-The-Field Measurement of Heart Rate: Correlates of Drug Use, Craving, Stress, and Mood in Polydrug Users",
author = "Ashley P. Kennedy and David H. Epstein and Michelle L. Jobes and Daniel Agage and Matthew Tyburski and Karran A. Phillips and Amin Ahsan Ali and Rummana Bari and Syed Monowar Hossain and Karen Hovsepian and Mahbubur Rahman and Emre Ertin and Santosh Kumar and Kenzie L. Preston",
url = "http://www.sciencedirect.com/science/article/pii/S0376871615001805",
doi = "http://dx.doi.org/10.1016/j.drugalcdep.2015.03.024",
issn = "0376-8716",
year = 2015,
date = "2015-04-07",
journal = "Drug and Alcohol Dependence",
number = 0,
pages = "-",
abstract = "AbstractBackground Ambulatory physiological monitoring could clarify antecedents and consequences of drug use and could contribute to a sensor-triggered mobile intervention that automatically detects behaviorally risky situations. Our goal was to show that such monitoring is feasible and can produce meaningful data. Methods We assessed heart rate (HR) with AutoSense, a suite of biosensors that wirelessly transmits data to a smartphone, for up to four weeks in 40 polydrug users in opioid-agonist maintenance as they went about their daily lives. Participants also self-reported drug use, mood, and activities on electronic diaries. We compared HR with self-report using multilevel modeling (SAS Proc Mixed). Results Compliance with AutoSense was good; the data yield from the wireless electrocardiographs was 85.7%. HR was higher when participants reported cocaine use than when they reported heroin use (F(2,9) = 250.3, p<.0001) and was also higher as a function of the dose of cocaine reported (F(1,8) = 207.7, p<.0001). HR was higher when participants reported craving heroin (F(1,16) = 230.9, p<.0001) or cocaine (F(1,14) = 157.2, p<.0001) than when they reported of not craving. HR was lower (p<.05) in randomly prompted entries in which participants reported feeling relaxed, feeling happy, or watching TV, and was higher when they reported feeling stressed, being hassled, or walking. Conclusions High-yield, high-quality heart-rate data can be obtained from drug users in their natural environment as they go about their daily lives, and the resultant data robustly reflect episodes of cocaine and heroin use and other mental and behavioral events of interest.",
keywords = "cocaine",
pubstate = "published",
tppubtype = "article"
}

@inproceedings{Ye2015,
title = "Detecting Bids for Eye Contact Using a Wearable Camera",
author = "Zhefan Ye and Yin L and Yun Liu and Chanel Bridges and Agata Rozga and James M. Rehg",
url = "http://cbi.gatech.edu/eyecontact/",
year = 2015,
date = "2015-05-04",
booktitle = "Detecting Bids for Eye Contact Using a Wearable Camera",
publisher = "IEEE",
organization = "11th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2015)",
abstract = "We propose a system for detecting bids for eye contact directed from a child to an adult who is wearing a point-of-view camera. The camera captures an egocentric view of the child-adult interaction from the adult’s perspective. We detect and analyze the child’s face in the egocentric video in order to automatically identify moments in which the child is trying to make eye contact with the adult. We present a learning-based method that couples a pose-dependent appearance model with a temporal Conditional Random Field (CRF). We present encouraging findings from an experimental evaluation using a newly collected dataset of 12 children. Our method outperforms state-of-the-art approaches and enables measuring gaze behavior in naturalistic social interactions.",
keywords = "eye tracking, wearable camera",
pubstate = "published",
tppubtype = "inproceedings"
}

@article{Rabbi2015,
title = "Automated Personalized Feedback for Physical Activity and Dietary Behavior Change With Mobile Phones: A Randomized Controlled Trial on Adults",
author = "M Rabbi and A Pfammatter and M Zhang and B Spring and T Choudhury",
url = "http://mhealth.jmir.org/2015/2/e42",
doi = "10.2196/mhealth.4160",
year = 2015,
date = "2015-05-14",
journal = "Journal of Medical Internet Research",
volume = 3,
number = 2,
pages = "e42",
abstract = "Background: A dramatic rise in health-tracking apps for mobile phones has occurred recently. Rich user interfaces make manual logging of users’ behaviors easier and more pleasant, and sensors make tracking effortless. To date, however, feedback technologies have been limited to providing overall statistics, attractive visualization of tracked data, or simple tailoring based on age, gender, and overall calorie or activity information. There are a lack of systems that can perform automated translation of behavioral data into specific actionable suggestions that promote healthier lifestyle without any human involvement. Objective: MyBehavior, a mobile phone app, was designed to process tracked physical activity and eating behavior data in order to provide personalized, actionable, low-effort suggestions that are contextualized to the user’s environment and previous behavior. This study investigated the technical feasibility of implementing an automated feedback system, the impact of the suggestions on user physical activity and eating behavior, and user perceptions of the automatically generated suggestions. Methods: MyBehavior was designed to (1) use a combination of automatic and manual logging to track physical activity (eg, walking, running, gym), user location, and food, (2) automatically analyze activity and food logs to identify frequent and nonfrequent behaviors, and (3) use a standard machine-learning, decision-making algorithm, called multi-armed bandit (MAB), to generate personalized suggestions that ask users to either continue, avoid, or make small changes to existing behaviors to help users reach behavioral goals. We enrolled 17 participants, all motivated to self-monitor and improve their fitness, in a pilot study of MyBehavior. In a randomized two-group trial, investigators randomly assigned participants to receive either MyBehavior’s personalized suggestions (n=9) or nonpersonalized suggestions (n=8), created by professionals, from a mobile phone app over 3 weeks. Daily activity level and dietary intake was monitored from logged data. At the end of the study, an in-person survey was conducted that asked users to subjectively rate their intention to follow MyBehavior suggestions. Results: In qualitative daily diary, interview, and survey data, users reported MyBehavior suggestions to be highly actionable and stated that they intended to follow the suggestions. MyBehavior users walked significantly more than the control group over the 3 weeks of the study (P=.05). Although some MyBehavior users chose lower-calorie foods, the between-group difference was not significant (P=.15). In a poststudy survey, users rated MyBehavior’s personalized suggestions more positively than the nonpersonalized, generic suggestions created by professionals (P

@inproceedings{Li_2015_CVPR,
author = "Yin Li and Zhefan Ye and James M. Rehg",
title = "Delving Into Egocentric Actions",
booktitle = "The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)",
year = 2015,
abstract = "We address the challenging problem of recognizing the camera wearer's actions from videos captured by an egocentric camera. Egocentric videos encode a rich set of signals regarding the camera wearer, including head movement, hand pose and gaze information. We propose to utilize these mid-level egocentric cues for egocentric action recognition. We present a novel set of egocentric features and show how they can be combined with motion and object features. The result is a compact representation with superior performance. In addition, we provide the first systematic evaluation of motion, object and egocentric cues in egocentric action recognition. Our benchmark leads to several surprising findings. These findings uncover the best practices for egocentric actions, with a significant performance boost over all previous state-of-the-art methods on three publicly available datasets.",
date = "2015-06-01",
pubstate = "published",
tppubtype = "inproceedings"
}

@article{Pelligrini2015,
title = "Smartphone applications to support weight loss: current perspectives",
author = "C.A. Pellegrini and A.F. Pfammatter and D.E. Conroy and B. Spring",
url = "http://www.ncbi.nlm.nih.gov/pubmed/26236766#",
year = 2015,
date = "2015-07-01",
journal = "Journal of Advanced Healthcare Technologies",
volume = "July 2015",
number = 1,
pages = "13-22",
abstract = "Lower cost alternatives are needed for the traditional in-person behavioral weight loss programs to overcome challenges of lowering the worldwide prevalence of overweight and obesity. Smartphones have become ubiquitous and provide a unique platform to aid in the delivery of a behavioral weight loss program. The technological capabilities of a smartphone may address certain limitations of a traditional weight loss program, while also reducing the cost and burden on participants, interventionists, and health care providers. Awareness of the advantages smartphones offer for weight loss has led to the rapid development and proliferation of weight loss applications (apps). The built-in features and the mechanisms by which they work vary across apps. Although there are an extraordinary number of a weight loss apps available, most lack the same magnitude of evidence-based behavior change strategies typically used in traditional programs. As features develop and new capabilities are identified, we propose a conceptual model as a framework to guide the inclusion of features that can facilitate behavior change and lead to reductions in weight. Whereas the conventional wisdom about behavior change asserts that more is better (with respect to the number of behavior change techniques involved), this model suggests that less may be more because extra techniques may add burden and adversely impact engagement. Current evidence is promising and continues to emerge on the potential of smartphone use within weight loss programs; yet research is unable to keep up with the rapidly improving smartphone technology. Future studies are needed to refine the conceptual model's utility in the use of technology for weight loss, determine the effectiveness of intervention components utilizing smartphone technology, and identify novel and faster ways to evaluate the ever-changing technology.",
keywords = "Diet, obesity, Physical activity, Technology",
pubstate = "published",
tppubtype = "article"
}

@article{Klasnja2015,
title = "Microrandomized trials: An experimental design for developing just-in-time adaptive interventions",
author = "P. Klasnja and E.B. Hekler and S. Shiffman and A. Boruvka and D. Almirall and A. Tewari and S.A. Murphy",
doi = "10.1037/hea0000305",
year = 2015,
date = "2015-12-01",
journal = "Health Psychology",
volume = "34 (suppl)",
pages = "1220-1228",
abstract = "Objective: This article presents an experimental design, the microrandomized trial, developed to support optimization of just-in-time adaptive interventions (JITAIs). JITAIs are mHealth technologies that aim to deliver the right intervention components at the right times and locations to optimally support individuals’ health behaviors. Microrandomized trials offer a way to optimize such interventions by enabling modeling of causal effects and time-varying effect moderation for individual intervention components within a JITAI. Method: The article describes the microrandomized trial design, enumerates research questions that this experimental design can help answer, and provides an overview of the data analyses that can be used to assess the causal effects of studied intervention components and investigate time-varying moderation of those effects. Results: Microrandomized trials enable causal modeling of proximal effects of the randomized intervention components and assessment of time-varying moderation of those effects. Conclusion: Microrandomized trials can help researchers understand whether their interventions are having intended effects, when and for whom they are effective, and what factors moderate the interventions’ effects, enabling creation of more effective JITAIs. (PsycINFO Database Record (c) 2015 APA, all rights reserved)",
keywords = "cardiac resynchronization therapy, Experimental design, Health Care Psychology, intervention, Mathematical Modeling, mobile devices, Technology, Telemedicine",
pubstate = "published",
tppubtype = "article"
}

@article{Small2014,
title = "Implantable device diagnostics on day of discharge identify heart failure patients at increased risk for early readmission for heart failure",
author = "R.S. Small and D.J. Whellan and A. Boyle and S. Sarkar and J. Koehler and E.N. Warman and W.T. Abraham",
url = "http://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24464745/",
year = 2014,
date = "2014-01-01",
journal = "European journal of heart failure",
volume = 16,
number = 4,
pages = "419--425",
publisher = "Wiley Online Library",
abstract = "AIMS: We hypothesized that diagnostic data in implantable devices evaluated on the day of discharge from a heart failure hospitalization (HFH) can identify patients at risk for HF readmission (HFR) within 30 days. METHODS AND RESULTS: In this retrospective analysis of four studies enrolling patients with CRT devices, we identified patients with a HFH, device data on the day of discharge, and 30-day post-discharge clinical follow-up. Four diagnostic criteria were evaluated on the discharge day: (i) intrathoracic impedance>8 ? below reference impedance; (ii) AF burden>6 h; (iii) CRT pacing<90%; and (iv) night heart rate>80 b.p.m. Patients were considered to have higher risk for HFR if ?2 criteria were met, average risk if 1 criterion was met, and lower risk if no criteria were met. A Cox proportional hazards model was used to compare the groups. The data cohort consisted of a total of 265 HFHs in 175 patients, of which 36 (14%) were followed by HFR. On the discharge day, ?2 criteria were met in 43 (16% of 265 HFHs), only 1 criterion was met in 92 (35%), and none of the four criteria were met in 130 HFHs (49%); HFR rates were 28, 16, and 7%, respectively. HFH with ?2 criteria met was five times more likely to have HFR compared with HFH with no criteria met (adjusted hazard ratio 5.0; 95% confidence interval 1.913.5",
keywords = "Early readmission risk, Heart failure, Implantable device diagnostics",
pubstate = "published",
tppubtype = "article"
}

@article{Balani2014,
title = "Distributed programming framework for fast iterative optimization in networked cyber-physical systems",
author = "R. Balani and L.F. Wanner and M.B. Srivastava",
url = "http://dl.acm.org/citation.cfm?id=2544386",
year = 2014,
date = "2014-01-01",
journal = "ACM Transactions on Embedded Computing Systems (TECS)",
volume = 13,
number = "2s",
pages = 66,
publisher = "ACM",
abstract = "Large-scale coordination and control problems in cyber-physical systems are often expressed within the networked optimization model. While significant advances have taken place in optimization techniques, their widespread adoption in practical implementations has been impeded by the complexity of internode coordination and lack of programming support for the same. Currently, application developers build their own elaborate coordination mechanisms for synchronized execution and coherent access to shared resources via distributed and concurrent controller processes. However, they typically tend to be error prone and inefficient due to tight constraints on application development time and cost. This is unacceptable in many CPS applications, as it can result in expensive and often irreversible side-effects in the environment due to inaccurate or delayed reaction of the control system. This article explores the design of a distributed shared memory (DSM) architecture that abstracts the details of internode coordination. It simplifies application design by transparently managing routing, messaging, and discovery of nodes for coherent access to shared resources. Our key contribution is the design of provably correct locality-sensitive synchronization mechanisms that exploit the spatial locality inherent in actuation to drive faster and scalable application execution through opportunistic data parallel operation. As a result, applications encoded in the proposed Hotline Application Programming Framework are error free, and in many scenarios, exhibit faster reactions to environmental events over conventional implementations. Relative to our prior work, this article extends Hotline with a new locality-sensitive coordination mechanism for improved reaction times and two tunable iteration control schemes for lower message costs. Our extensive evaluation demonstrates that realistic performance and cost of applications are highly sensitive to the prevalent deployment, network, and environmental characteristics. This highlights the importance of Hotline, which provides user-configurable options to trivially tune these metrics and thus affords time to the developers for implementing, evaluating, and comparing multiple algorithms.",
keywords = "Algorithms, Design, distributed optimization, distributed shared memory, Performance, subgradient methods, synchronization, Wireless sensor/actuator networks",
pubstate = "published",
tppubtype = "article"
}

@article{Zheng2014,
title = "Ensuring Predictable Contact Opportunity for Scalable Vehicular Internet Access On the Go",
author = "Z. Zheng and Z. Lu and P. Sinha and S. Kumar",
url = "http://arxiv.org/pdf/1401.0781v1",
year = 2014,
date = "2014-01-04",
publisher = "IEEE",
abstract = "With increasing popularity of media enabled handhelds and their integration with the in-vehicle entertainment systems, the need for high data-rate services for mobile users on the go is evident. This ever-increasing demand of data is constantly surpassing what cellular networks can economically support. Large-scale Wireless LANs (WLANs) can provide such a service, but they are expensive to deploy and maintain. Open WLAN access-points, on the other hand, need no new deployments, but can offer only opportunistic services, lacking any performance guarantees. In contrast, a carefully planned sparse deployment of roadside WiFi provides an economically scalable infrastructure with quality of service assurance to mobile users. In this paper, we present a new metric, called Contact Opportunity, to closely model the quality of data service that a mobile user might experience when driving through the system. We then present efficient deployment algorithms for minimizing the cost for ensuring a required level of contact opportunity. We further extend this concept and the deployment techniques to a more intuitive metric – the average throughput – by taking various dynamic elements into account. Simulations over a real road network and experimental results show that our approach achieves significantly better cost vs. throughput tradeoff in both the worst case and average case compared with some commonly used deployment algorithms.",
keywords = "Contact Opportunity, infrastructure, LANs, media-enabled handhelds, smartphones, WiFi",
pubstate = "published",
tppubtype = "article"
}

@article{rejeski2014,
title = "A group-mediated, home-based physical activity intervention for patients with peripheral artery disease: effects on social and psychological function",
author = "W.J. Rejeski and B. Spring and K. Domanchuk and H. Tao and L. Tian and L. Zhao and M.Z.M. McDermott",
url = "http://www.ncbi.nlm.nih.gov/pubmed/24467875",
year = 2014,
date = "2014-01-28",
journal = "Journal of Translational Medicine",
abstract = "BACKGROUND: PAD is a disabling, chronic condition of the lower extremities that affects approximately 8 million people in the United States. The purpose of this study was to determine whether an innovative home-based walking exercise program for patients with peripheral artery disease (PAD) improves self-efficacy for walking, desire for physical competence, satisfaction for physical functioning, social functioning, and acceptance of PAD related pain and discomfort. METHODS: The design was a 6-month randomized controlled clinical trial of 194 patients with PAD. Participants were randomized to 1 of 2 parallel groups: a home-based group-mediated cognitive behavioral walking intervention or an attention control condition. RESULTS: Of the 194 participants randomized, 178 completed the baseline and 6-month follow-up visit. The mean age was 70.66 (±9.44) and was equally represented by men and women. Close to half of the cohort was African American. Following 6-months of treatment, the intervention group experienced greater improvement on self-efficacy (p = .0008), satisfaction with functioning (p = .0003), pain acceptance (p = .0002), and social functioning (p = .0008) than the control group; the effects were consistent across a number of potential moderating variables. Change in these outcomes was essentially independent of change in 6-minute walk performance.",
keywords = "Group-mediated intervention, Peripheral artery disease, Physical activity, Psychological function, Social function",
pubstate = "published",
tppubtype = "article"
}

@article{nahumshani2014a,
title = "Supervisor support: does supervisor support buffer or exacerbate the adverse effects of supervisor undermining?",
author = "I. Nahum-Shani and M.M. Henderson and S. Lim and A.D. Vinokur",
url = "http://www.ncbi.nlm.nih.gov/pubmed/24490969",
year = 2014,
date = "2014-02-03",
journal = "Journal of Applied Psychology",
volume = 99,
number = 3,
pages = "484-503",
abstract = "Empirical investigations concerning the interplay between supervisor support and supervisor undermining behaviors and their effects on employees yielded contradictory findings, with some studies suggesting that support buffers the adverse effects of undermining, and others suggesting that support exacerbates these adverse effects. Seeking to explain such contradictory findings, we integrate uncertainty-management perspectives with coping theory to posit that relational uncertainty is inherent in the mixture of supervisor support and undermining. Hence, whether supervisor support buffers or exacerbates the adverse effects of supervisor undermining on employee health and well-being depends on factors pertaining to employee ability to resolve and manage such relational uncertainty. Specifically, we hypothesize a buffering effect for employees with high self-esteem and high quality of work life, and an exacerbating effect for employees with low self-esteem and low quality of work life. Analyses of 2-wave data collected from a probability stratified sample of U.S. Air Force personnel supported our predictions. Two supplementary studies of the U.S. military replicated our core findings and demonstrated its practical significance.",
keywords = "employee behavior, supervisor support of employees, undermining behavior",
pubstate = "published",
tppubtype = "article"
}

@article{Hnata,
title = "Macroprogramming: Lowering the Entry Barrier for Wireless Embedded Network Systems",
author = "T.W. Hnat",
url = "http://link.springer.com/chapter/10.1007/978-3-319-04651-8_11",
year = 2014,
date = "2014-02-17",
journal = "Wireless Sensor Networks",
volume = 8354,
pages = "166-181",
publisher = "Springer",
abstract = "Accurate energy expenditure monitoring will be an essential part of medical diagnosis in the future, enabling individually-tailored just-in-time interventions. However, there are currently no real-time monitors that are practical for continuous daily use. In this paper, we introduce the K-Sense energy expenditure monitor that uses inertial measurement units (IMUs) mounted to an individuals wrist and ankle with elastic bands to determine angular velocity and position. The system utilizes kinematics to determine the amount of energy required for each limb to achieve its current movement. Our empirical evaluation includes over 3,000,000 individual data samples across 12 individuals and the results indicate that the system can estimate total energy expenditure with a 92 percent accuracy on average.",
keywords = "Body Sensor Network, Energy Expenditure, Kinematics",
pubstate = "published",
tppubtype = "article"
}

U Kang, L Akoglu and D H Chau. Big Graph Mining for the Web and Social Media: Algorithms, Anomaly Detection, and Applications. In Proceedings of the 7th ACM International Conference on Web Search and Data Mining. 2014, 677–678. URLBibTeX

@inproceedings{Kang:2014:BGM:2556195.2556198,
title = "Big Graph Mining for the Web and Social Media: Algorithms, Anomaly Detection, and Applications",
author = "U. Kang and L. Akoglu and D.H. Chau",
url = "http://doi.acm.org/10.1145/2556195.2556198",
isbn = "978-1-4503-2351-2",
year = 2014,
date = "2014-02-24",
booktitle = "Proceedings of the 7th ACM International Conference on Web Search and Data Mining",
pages = "677--678",
publisher = "ACM",
address = "New York, New York, USA",
series = "WSDM '14",
abstract = "Graphs are everywhere: social networks, computer net- works, mobile call networks, the World Wide Web, protein interaction networks, and many more. The lower cost of disk storage, the success of social networking websites and Web 2.0 applications, and the high availability of data sources lead to graphs being generated at unprecedented size. They are now measured in terabytes or even petabytes, with more than billions of nodes and edges. Finding patterns on large graphs have a lot of applica- tions including cyber security on the Web, social media min- ing (Facebook, Twitter), and fraud detection, among others. This tutorial will cover topics related to finding patterns and anomalies and sensemaking in large-scale graphs with appli- cations to real-world problems in social media and the Web. Specifically, we aim to answer the following questions: How can we scale up graph mining algorithms for massive graphs with billions of edges? How can we find anomalies in such large-scale graphs? How can we make sense of disk-resident large graphs, what and how can we do visual analytics? How can we use the algorithms and anomaly detection techniques to solve challenging real-world problems that play key role in social media and the Web? Our tutorial consists of three main parts. We start with scalable graph mining algorithms for billion-scale graphs, in- cluding structure analysis, eigensolvers, storage and index- ing, and graph layout and graph compression. Next we de- scribe anomaly detection techniques for large scale graphs with applications on social media. Finally, we discuss vi- sual analytics techniques which leverage these algorithms and anomaly detection techniques in the previous parts.",
keywords = "anomaly detection, graph mining, hadoop, mapreduce, Visual analytics",
pubstate = "published",
tppubtype = "inproceedings"
}

D S Lopez, M E Fernandez, M A Cano, C Mendez, C L Tsai, D W Wetter and S S Strom. Association of acculturation, nativity, and years living in the United States with biobanking among individuals of Mexican descent.. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, 2014. URLBibTeX

@article{Lopez2014,
title = "Association of acculturation, nativity, and years living in the United States with biobanking among individuals of Mexican descent.",
author = "D.S. Lopez and M.E. Fernandez and M.A. Cano and C. Mendez and C.L. Tsai and D.W. Wetter and S.S. Strom",
url = "http://www.ncbi.nlm.nih.gov/pubmed/24609849",
year = 2014,
date = "2014-03-01",
journal = "Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology",
abstract = {BACKGROUND: Biobanking is the collection of human biospecimens (tissues, blood, and body fluids) and their associated clinical and outcome data. Hispanics are less likely to provide biologic specimens for biobanking. The purpose of this study was to investigate the association of acculturation, nativity status, and years living in the United States with participation in biobanking among individuals of Mexican descent. METHODS: Participants were 19,212 adults of Mexican descent enrolled in an ongoing population-based cohort in Houston, TX. Participants were offered the opportunity to provide a blood, urine, or saliva sample for biobanking. Acculturation was assessed with the bidimensional acculturation scale for Hispanics and scores were categorized into "low acculturation," "bicultural," and "high-acculturation." RESULTS: After multivariable adjustment, we found an increased likelihood of participation in biobanking among individuals classified as "bicultural" as compared with "highly acculturated" individuals [OR, 1.58; 95% confidence intervals (CI), 1.10-2.26]. The associations of nativity status and years living in the United States with biobanking were not statistically significant. After stratifying by gender, the associations of acculturation, nativity status, and years living in the United States with biobanking were not statistically significant. CONCLUSION: Although individuals of Mexican descent who were "bicultural" were more likely to participate in biobanking than individuals who were "highly acculturated," the difference in rates of participation among acculturation categories was small. The high participation rate in biospecimen collection is likely due to extensive community-engaged research efforts. Future studies are warranted to understand individuals' participation in biobanking. IMPACT: Community-engaged research efforts may increase Hispanics' participation in biobanking.},
keywords = "acculturation, biobanking, Mexican-Americans",
pubstate = "published",
tppubtype = "article"
}

@article{BARU2014,
title = "Trident: Visioning A Shared Infrastructure For Data Research At Scale",
author = "C. Baru and M. Carey and T. Condie and V. Hristidis and D. Lifka and R. Wolski and S. Rajan and A. Roy",
url = "http://www.nist.gov/itl/iad/upload/proceedings_1-2.pdf",
year = 2014,
date = "2014-03-04",
journal = "Data Science Symposium",
pages = 20,
abstract = "In this talk, we identify the need for a shared infrastructure for data research at scale, and provide a vision for addressing this need. Scalable data management is a computer science endeavor that is currently enjoying widespread interest and a sizable industry investment. There is a pressing need to establish objective and scientific approaches to big data and data science research by providing a common platform for software experimentation. Such a platform could enable objective benchmarking and comparative analysis of software and algorithmic performance thereby improving the current situation where most researchers work on different computing platforms using different algorithms, different data, and different environmental settings.",
keywords = "benchmarking, Big Data, scalable data management, shared research infrastructure",
pubstate = "published",
tppubtype = "article"
}

@inproceedings{6813945,
title = "Inferring occupancy from opportunistically available sensor data",
author = "L. Yang and K. Ting and M.B. Srivastava",
url = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6813945",
year = 2014,
date = "2014-03-24",
booktitle = "2014 IEEE International Conference on Pervasive Computing and Communications (PerCom)",
pages = "60-68",
abstract = "Commercial and residential buildings are usually instrumented with meters and sensors that are deployed as part of a utility infrastructure installed by companies that provide services such as electricity, water, gas, security, phone, etc. As part of their normal operation, these service providers have direct access to information from the sensors and meters. A concern arises that the sensory information collected by the providers, although coarse-grained, can be subject to analysis that reveals private information about the users of the building. Oftentimes, multiple services are provided by the same company, in which case the potential for leakage of private information increases. Our research seeks to investigate the extent to which easily available sensory information may be used by external service providers to make occupancy-related inferences. Particularly, we focus on inferences from two different sources: motion sensors, which are installed and monitored by security companies, and smart electric meters, which are deployed by electric companies for billing and demand-response management. We explore the motion sensor scenario in a three-person single-family home and the electric meter scenario in a twelve-person university lab. Our exploration with various inference methods shows that sensory information available to service providers can enable them to make undesired occupancy related inferences, such as levels of occupancy or even the identities of current occupants, significantly better than naive prediction strategies that do not make use of sensor information.",
keywords = "building management systems;buildings (structures);control engineering computing;security of data;sensors;smart meters;billing;commercial buildings;demand-response management;electric company;external service provider;motion sensor;occupancy-related inference;opportunistically available sensor data;",
pubstate = "published",
tppubtype = "inproceedings"
}

@article{Rebar2014,
title = "Habits predict physical activity on days when intentions are weak.",
author = "A.L. Rebar and S. Elavsky and J.P. Maher and S.E. Doerksen and D.E. Conroy",
url = "http://www.researchgate.net/profile/Amanda_Rebar/publication/261256626_Habits_predict_physical_activity_on_days_when_intentions_are_weak/links/53f444ae0cf2155be354f8e2.pdf",
year = 2014,
date = "2014-04-01",
journal = "Journal of Sport & Exercise Psychology",
volume = 36,
number = 2,
pages = "157--165",
abstract = "Physical activity is regulated by controlled processes, such as intentions, and automatic processes, such as habits. Intentions relate to physical activity more strongly for people with weak habits than for people with strong habits, but people's intentions vary day by day. Physical activity may be regulated by habits unless daily physical activity intentions are strong. University students (N = 128) self-reported their physical activity habit strength and subsequently self-reported daily physical activity intentions and wore an accelerometer for 14 days. On days when people had intentions that were weaker than typical for them, habit strength was positively related to physical activity, but on days when people had typical or stronger intentions than was typical for them, habit strength was unrelated to daily physical activity. Efforts to promote physical activity may need to account for habits and the dynamics of intentions.",
keywords = "accelerometer, habit strength, habits, Physical activity",
pubstate = "published",
tppubtype = "article"
}

@article{Maher2014,
title = "Daily satisfaction with life is regulated by both physical activity and sedentary behavior.",
author = "J.P. Maher and S.E. Doerksen and S. Elavsky and D.E. Conroy",
url = "http://www.ncbi.nlm.nih.gov/pubmed/24686953",
year = 2014,
date = "2014-04-01",
journal = "Journal of Sport & Exercise Psychology",
volume = 36,
number = 2,
pages = "166--178",
abstract = "Recent research revealed that on days when college students engage in more physical activity than is typical for them, they also experience greater satisfaction with life (SWL). That work relied on self-reported physical activity and did not differentiate between low levels of physical activity and sedentary behavior. This study was designed to (1) determine if the association between self-reported physical activity and SWL would exist when physical activity was monitored objectively and (2) examine the between- and within-person associations among physical activity, sedentary behavior, and SWL. During a 14-day ecological momentary assessment study, college students (N = 128) wore an accelerometer to objectively measure physical activity and sedentary behavior, and they self-reported their physical activity, sedentary behavior, and SWL at the end of each day. Physical activity and sedentary behavior had additive, within-person associations with SWL across self-reported and objective-measures of behavior. Strategies to promote daily well-being should encourage college students to incorporate greater amounts of physical activity as well as limit their sedentary behavior.",
keywords = "Exercise, life satisfaction, Physical activity",
pubstate = "published",
tppubtype = "article"
}

@inproceedings{Chakraborty2014a,
author = "S. Chakraborty and C. Shen and K.R. Raghavan and Y. Shoukry and M. Millar and M.B. Srivastava",
title = "ipShield: a framework for enforcing context-aware privacy",
booktitle = "Proceedings of the 11th USENIX Conference on Networked Systems Design and Implementation",
year = 2014,
pages = "143--156",
organization = "USENIX Association",
abstract = "Smart phones are used to collect and share personal data with untrustworthy third-party apps, often leading to data misuse and privacy violations. Unfortunately, state-of-the-art privacy mechanisms on Android provide inadequate access control and do not address the vulnerabilities that arise due to unmediated access to so-called innocuous sensors on these phones. We present ipShield, a framework that provides users with greater control over their resources at runtime. ipShield performs monitoring of every sensor accessed by an app and uses this information to perform privacy risk assessment. The risks are conveyed to the user as a list of possible inferences that can be drawn using the shared sensor data. Based on user-configured lists of allowed and private inferences, a recommendation consisting of binary privacy actions on individual sensors is generated. Finally, users are provided with options to override the recommended actions and manually configure context-aware fine-grained privacy rules. We implemented ipShield by modifying the AOSP on a Nexus 4 phone. Our evaluation indicates that running ipShield incurs negligible CPU and memory overhead and only a small reduction in battery life.",
date = "2014-04-02",
keywords = "data privacy, ipShield, personal data, sensors, smartphones",
pubstate = "published",
tppubtype = "inproceedings",
url = "https://www.usenix.org/conference/nsdi14/technical-sessions/presentation/chakraborty"
}

@article{alabsi2014,
title = "Concurrent tobacco and khat use is associated with blunted cardiovascular stress response and enhanced negative mood: a cross-sectional investigation",
author = "M. al'Absi and M. Nakajima and A. Dokam and A. Sameai and M. Alsoofi and N.S. Khalil and M. Al Habori",
url = "http://onlinelibrary.wiley.com/doi/10.1002/hup.2403/full",
year = 2014,
date = "2014-04-07",
journal = "Human Psychophmarmacology",
volume = 29,
number = 4,
pages = "307-314",
abstract = "Objectives Khat (Catha edulis), an amphetamine-like plant, is widely used in East Africa and the Arabian Peninsula and is becoming a growing problem in other parts of the world. The concurrent use of tobacco and khat is highly prevalent and represents a public health challenge. We examined for the first time associations of the concurrent use of tobacco and khat with psychophysiological responses to acute stress in two sites in Yemen. Methods Participants (N?=?308; 135 women) included three groups: users of khat and tobacco, users of khat alone, and a control group (nonsmokers/nonusers of khat). These individuals completed a laboratory session in which blood pressures (BP), heart rate, and mood measures were assessed during rest and in response to acute stress. Results Concurrent use of khat and tobacco was associated with attenuated systolic BP, diastolic BP, and heart rate responses to laboratory stress (ps?0.05) and with increased negative affect relative to the control group (ps?0.05). Conclusions Results demonstrated blunted cardiovascular responses to stress and enhanced negative affect in concurrent khat and tobacco users. These findings extend previous studies with other substances and suggest that adverse effects of khat use may lie in its association with the use of tobacco.",
keywords = "cardiovascular response, Khat, negative affect, psychopharmacology, tobacco",
pubstate = "published",
tppubtype = "article"
}

@article{Hossain2014a,
title = "Identifying Drug (Cocaine) Intake Events from Acute Physiological Response in the Presence of Free-living Physical Activity.",
author = "S.M. Hossain and A.A. Ali and M.M. Rahman and E. Ertin and D. Epstein and A. Kennedy and K. Preston and A. Umbricht and Y. Chen and S. Kumar",
url = "http://dl.acm.org/ft_gateway.cfm?id=2602348&ftid=1444466&dwn=1&CFID=494348324&CFTOKEN=58863845",
year = 2014,
date = "2014-04-15",
journal = "Proceedings of the 13th ACM/IEE Conference on Information Processing in Sensor Networks",
volume = 2014,
pages = "71--82",
institution = "Dept of Computer Science and Engg., Washington University in St. Louise.",
abstract = "A variety of health and behavioral states can potentially be inferred from physiological measurements that can now be collected in the natural free-living environment. The major challenge, however, is to develop computational models for automated detection of health events that can work reliably in the natural field environment. In this paper, we develop a physiologically-informed model to automatically detect drug (cocaine) use events in the free-living environment of participants from their electrocardiogram (ECG) measurements. The key to reliably detecting drug use events in the field is to incorporate the knowledge of autonomic nervous system (ANS) behavior in the model development so as to decompose the activation effect of cocaine from the natural recovery behavior of the parasympathetic nervous system (after an episode of physical activity). We collect 89 days of data from 9 active drug users in two residential lab environments and 922 days of data from 42 active drug users in the field environment, for a total of 11,283 hours. We develop a model that tracks the natural recovery by the parasympathetic nervous system and then estimates the dampening caused to the recovery by the activation of the sympathetic nervous system due to cocaine. We develop efficient methods to screen and clean the ECG time series data and extract candidate windows to assess for potential drug use. We then apply our model on the recovery segments from these windows. Our model achieves 100% true positive rate while keeping the false positive rate to 0.87/day over (9+ hours/day of) lab data and to 1.13/day over (11+ hours/day of) field data.",
keywords = "Drug Event Detection, electrocardiogram, Wearable Sensors",
pubstate = "published",
tppubtype = "article"
}

@article{Yonekura2014,
title = "Relationship between salivary cortisol and depression in adolescent survivors of a major natural disaster",
author = "T. Yonekura and K. Takeda and V. Shetty and M. Yamaguchi",
url = "http://link.springer.com/content/pdf/10.1007%2Fs12576-014-0315-x.pdf",
year = 2014,
date = "2014-04-18",
journal = "The Journal of Physiological Sciences",
pages = "1--7",
publisher = "Springer",
abstract = "The purpose of this study was to determine the utility of salivary cortisol levels for screening mental states such as depression in adolescents following a natural disaster. We examined the relationship of salivary cortisol levels in adolescent survivors of the 2011 Tohoku Earthquake with the depression subscale of the 28-item General Health Questionnaire (GHQ). Subjects were 63 adolescent survivors (age = 14.29 years ± 0.51) who were administered the GHQ and provided saliva samples thrice daily (morning, afternoon and evening) over the course of 3 days. Based on the GHQ-depression subscores, subjects were divided into low and high depression groups. About 22 % of the subjects were classified into the high symptom group. When data collected over 3 days were used, a significant difference was observed between the two groups in the salivary cortisol levels at the evening time point as well the ratio of the morning/evening levels (p < 0.05). Analyzed by means of receiver-operating characteristic curves, the morning/evening ratios showed a good power in discriminating between subjects with and without depressive symptoms. Our study suggests that repeated measurement of salivary cortisol levels over 3 days has utility in screening for depressive states in adolescents following a natural disaster.",
keywords = "Adolescents, Cortisol, Depression, GHQ, Natural disasters",
pubstate = "published",
tppubtype = "article"
}

@article{Rahman2014b,
title = "Turning the Tide: Curbing Deceptive Yelp Behaviors",
author = "M. Carbunar and B. Ballesteros and J. Burri and D.H. Chau and D. Rahman",
url = "http://users.cis.fiu.edu/~carbunar/deceptive.pdf",
year = 2014,
date = "2014-04-24",
journal = "Prodeedings of SIAM International Conference on Data Mining (SDM)",
abstract = "The popularity and influence of reviews, make sites like Yelp ideal targets for malicious behaviors. We present Marco, a novel system that exploits the unique combination of social, spatial and temporal signals gleaned from Yelp, to detect venues whose ratings are impacted by fraudulent reviews. Marco increases the cost and complexity of attacks, by imposing a tradeoff on fraudsters, between their ability to impact venue ratings and their ability to remain undetected. We contribute a new dataset to the community, which consists of both ground truth and gold standard data. We show that Marco significantly outperforms state-of-the-art approaches, by achieving 94% accuracy in classifying reviews as fraudulent or genuine, and 95.8% accuracy in classifying venues as deceptive or legitimate. Marco successfully flagged 244 deceptive venues from our large dataset with 7,435 venues, 270,121 reviews and 195,417 users. Among the San Francisco car repair and moving companies that we analyzed, almost 10% exhibit fraudulent behaviors.",
keywords = "detection of fraudulent reviews, malicious behaviors, Marco, Yelp",
pubstate = "published",
tppubtype = "article"
}

@inproceedings{Stolper2014a,
title = "GLOs: graph-level operations for exploratory network visualization",
author = "C.D. Stolper and F. Foerster and M. Kahng and Z. Lin and A. Goel and J. Stasko and D.H. Chau",
url = "http://www.cc.gatech.edu/~dchau/glo/glo_chi2014.pdf",
year = 2014,
date = "2014-04-26",
booktitle = "2014 ACM CHI Conference on Human Factors in Computing Systems (CHI 2014)",
pages = "1375--1380",
organization = "ACM",
abstract = "There is a wealth of visualization techniques available for graph and network visualization. However, each of these techniques was designed for a specific task. Many graph visualization techniques and the transitions between them can be specified using a set of operations on the visualization elements such as positioning or resizing nodes, showing or hiding edges, or showing or hiding axes. We term these operations Graph-Level Operations or GLOs. Our goal is to identify and provide a comprehensive set of these operations in order to better support the broadest range of graph and network analysis tasks. Here we present early results of our work, including a preliminary set of operations and an example application of GLOs in transitioning between familiar graph visualization techniques.",
keywords = "Graphs; visualization techniques; operations",
pubstate = "published",
tppubtype = "inproceedings"
}

@inproceedings{Saravanan2014,
title = "LatentGesture: active user authentication through background touch analysis",
author = "P. Saravanan and S. Clarke and D.H. Chau and H. Zha",
url = "http://dl.acm.org/citation.cfm?doid=2592235.2592252",
year = 2014,
date = "2014-04-26",
booktitle = "Proceedings of the Second International Symposium of Chinese CHI",
pages = "110--113",
organization = "ACM",
abstract = "We propose a new approach for authenticating users of mobile devices that is based on analyzing the user’s touch interaction with common user interface (UI) elements, e.g., buttons, checkboxes and sliders. Unlike one-off authentication techniques such as passwords or gestures, our technique works continuously in the background while the user uses the mobile device. To evaluate our approach’s effectiveness, we conducted a lab study with 20 participants, where we recorded their interaction traces on a mobile phone and a tablet (e.g., touch pressure, locations), while they filled out electronic forms populated with UI widgets. Using classification methods based on SVM and Random Forests, we achieved an average of 97.9% accuracy with a mobile phone and 96.79% accuracy with a tablet for single user classification, demonstrating that our technique has strong potential for real-world use. We believe our research can help strengthen personal device security and safeguard against unintended or unauthorized uses, such as small children in a household making unauthorized online transactions on their parents’ devices, or an impostor accessing the bank account belonging to the victim of a stolen device.",
keywords = "Active authentication, classification model., fraudulent transactions, shoulder surfing, touch gestures",
pubstate = "published",
tppubtype = "inproceedings"
}

@article{allen2014smoking,
title = "Smoking- and menstrual-related symptomatology during short-term smoking abstinence by menstrual phase and depressive symptoms",
author = "S.S. Allen and A.M. Allen and N. Tosun and S. Lunos and M. al'Absi and D. Hatsukami",
url = "http://www.sciencedirect.com/science/article/pii/S0306460314000318",
year = 2014,
date = "2014-05-01",
journal = "Elsevier Addictive Behaviors",
volume = 39,
number = 5,
pages = "901-906",
abstract = "Menstrual phase and depressive symptoms are known to minimize quit attempts in women. Therefore, the influence of these factors on smoking- and menstrual-related symptomatology during acute smoking cessation was investigated in a controlled cross-over lab-study. Participants (n = 147) completed two six-day testing weeks during their menstrual cycle with testing order randomly assigned (follicular vs. luteal). The testing week consisted of two days of ad libitum smoking followed by four days of biochemically verified smoking abstinence. Daily symptomatology measures were collected. Out of the 11 total symptoms investigated, six were significantly associated with menstrual phase and nine were significantly associated with level of depressive symptoms. Two significant interactions were noted indicating that there may be a stronger association between depressive symptoms with negative affect and premenstrual pain during the follicular phase compared to the luteal phase. Overall, these observations suggest that during acute smoking abstinence in premenopausal smokers, there is an association between depressive symptoms and symptomatology whereas menstrual phase appears to have less of an effect. Further study is needed to determine the effect of these observations on smoking cessation outcomes, as well as to define the mechanism of menstrual phase and depressive symptoms on smoking-related symptomatology.",
keywords = "Smoking cessation; Menstrual cycle; Depressive symptoms; Withdrawal",
pubstate = "published",
tppubtype = "article"
}

@article{cano2014positive,
title = "Positive smoking outcome expectancies mediate the association between negative affect and smoking urge among women during a quit attempt.",
author = "M.Á. Cano and C.Y. Lam and M. Chen and C.E. Adams and V. Correa-Fernández and D.W. Stewart and J.B. McClure and P.M. Cinciripini and D.W. Wetter",
url = "http://www.ncbi.nlm.nih.gov/pubmed/24796849",
year = 2014,
date = "2014-05-05",
journal = "Experiential and Clinical Psychopharmacology",
volume = 22,
number = 4,
pages = "322-40",
abstract = "Ecological momentary assessment was used to examine associations between negative affect, positive smoking outcome expectancies, and smoking urge during the first 7 days of a smoking quit attempt. Participants were 302 female smokers who enrolled in an individually tailored smoking cessation treatment study. Multilevel mediation analysis was used to examine the temporal relationship among the following: (a) the effects of negative affect and positive smoking outcome expectancies at 1 assessment point (e.g., time j) on smoking urge at the subsequent time point (e.g., time j + 1) in Model 1; and, (b) the effects of negative affect and smoking urge at time j on positive smoking outcome expectancies at time j + 1 in Model 2. The results from Model 1 showed a statistically significant effect of negative affect at time j on smoking urge at time j + 1, and this effect was mediated by positive smoking outcome expectancies at time j, both within- and between-participants. In Model 2, the within-participant indirect effect of negative affect at time j on positive smoking outcome expectancies at time j + 1 through smoking urge at time j was nonsignificant. However, a statistically significant indirect between-participants effect was found in Model 2. The findings support the hypothesis that urge and positive smoking outcome expectancies increase as a function of negative affect, and suggest a stronger effect of expectancies on urge as opposed to the effect of urge on expectancies.",
keywords = "ecological momentary assessment, positive smoking outcome, smoking cessation, smoking urge",
pubstate = "published",
tppubtype = "article"
}

@article{Conroy2014,
title = "Behavior Change Techniques in Top-Ranked Mobile Apps for Physical Activity",
author = "D.E. Conroy and C. Yang and J.P. Maher",
url = "http://www.ajpmonline.org/pb/assets/raw/Health%20Advance/journals/amepre/AMEPRE_4004_Embargo.pdf",
year = 2014,
date = "2014-05-06",
journal = "American journal of preventive medicine",
volume = 46,
number = 6,
pages = "649--652",
publisher = "Elsevier",
abstract = "Background: Mobile applications (apps) have potential for helping people increase their physical activity, but little is known about the behavior change techniques marketed in these apps. Purpose: The aim of this study was to characterize the behavior change techniques represented in online descriptions of top-ranked apps for physical activity. Methods: Top-ranked apps (n¼167) were identified on August 28, 2013, and coded using the Coventry, Aberdeen and London–Revised (CALO-RE) taxonomy of behavior change techniques during the following month. Analyses were conducted during 2013. Results: Most descriptions of apps incorporated fewer than four behavior change techniques. The most common techniques involved providing instruction on how to perform exercises, modeling how to perform exercises, providing feedback on performance, goal-setting for physical activity, and planning social support/change. A latent class analysis revealed the existence of two types of apps, educational and motivational, based on their configurations of behavior change techniques. Conclusions: Behavior change techniques are not widely marketed in contemporary physical activity apps. Based on the available descriptions and functions of the observed techniques in contemporary health behavior theories, people may need multiple apps to initiate and maintain behavior change. This audit provides a starting point for scientists, developers, clinicians, and consumers to evaluate and enhance apps in this market.",
keywords = "behavior change techniques, mobile apps, mobile health, Physical activity",
pubstate = "published",
tppubtype = "article"
}

@article{Batra2014a,
title = "NILMTK: An Open Source Toolkit for Non-intrusive Load Monitoring",
author = "N. Batra and J. Kelly and O. Parson and H. Dutta and W. Knottenbelt and A. Rogers and A. Singh and M.B. Srivastava",
url = "http://arxiv.org/abs/1404.3878",
year = 2014,
date = "2014-05-15",
journal = "arXiv.org (preprint arXiv:1404.3878)",
abstract = "Non-intrusive load monitoring, or energy disaggregation, aims to separate household energy consumption data collected from a single point of measurement into appliance-level consumption data. In recent years, the field has rapidly expanded due to increased interest as national deployments of smart meters have begun in many countries. However, empirically comparing disaggregation algorithms is currently virtually impossible. This is due to the different data sets used, the lack of reference implementations of these algorithms and the variety of accuracy metrics employed. To address this challenge, we present the Non-intrusive Load Monitoring Toolkit (NILMTK); an open source toolkit designed specifically to enable the comparison of energy disaggregation algorithms in a reproducible manner. This work is the first research to compare multiple disaggregation approaches across multiple publicly available data sets. Our toolkit includes parsers for a range of existing data sets, a collection of preprocessing algorithms, a set of statistics for describing data sets, two reference benchmark disaggregation algorithms and a suite of accuracy metrics. We demonstrate the range of reproducible analyses which are made possible by our toolkit, including the analysis of six publicly available data sets and the evaluation of both benchmark disaggregation algorithms across such data sets.",
keywords = "energy disaggregation, non-intrusive load monitoring, smart meters",
pubstate = "published",
tppubtype = "article"
}

@inproceedings{6875722,
title = "Waveform-diverse MIMO imaging radar target measurements",
author = "K.B. Stewart and N. Majurec and R.J. Burkholder and E. Ertin and J.T. Johnson",
url = "http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=6875722&abstractAccess=no&userType=inst",
year = 2014,
date = "2014-05-19",
booktitle = "2014 IEEE Radar Conference",
pages = "0918-0922",
abstract = "The construction and testing of a MIMO radar for target imaging are presented. Two distinct radar systems are combined in order to form a four-channel platform with operation validated through a series of stationary target measurements. These are conducted using both time-diverse and waveform-diverse channel separation methods, and imaging results from each are compared to evaluate the performance of two varieties of pseudo-noise waveforms and the LFM chirp. Results from the first experiment successfully demonstrate the operation of the radar system but leave open a number of scientific questions for further investigation in a revised experiment. Conclusions from this second measurement are to be presented in this year's radar conference.",
keywords = "Antenna arrays, Antenna measurements, four-channel platform, Imaging, LFM chirp, MIMO, MIMO radar, pseudo-noise waveforms, Radar imaging, stationary target measurement, time-diverse channel separation method, waveform-diverse channel separation method, waveform-diverse MIMO imaging radar target measurement",
pubstate = "published",
tppubtype = "inproceedings"
}

@article{Potretzke2014,
title = "Changes in circulating leptin levels during acute stress and associations with craving in abstinent smokers: a preliminary investigation",
author = "S. Potretzke and M. Nakajima and T. Cragin and M. al' Absi",
url = "http://www.ncbi.nlm.nih.gov/pubmed/24954303",
year = 2014,
date = "2014-05-24",
journal = "Psychoneuroendocrinology",
volume = 47,
pages = "232-40",
abstract = "Recent research suggests a role for the appetite hormone leptin in cigarette smoking. This study examined patterns of change in leptin in response to stress and associations with craving during the initial phase of a quit attempt. Thirty-six smokers (average age±SEM, 33.4±2.4) interested in smoking cessation set a quit day and were required to be abstinent for 24h. After, they completed a laboratory session including public speaking and cognitive challenges, and attended 4 weekly post-cessation assessments. Blood samples and self-report measures were collected throughout the laboratory session. The results indicated that leptin levels significantly increased following exposure to acute stress. We also found positive correlations between leptin and craving for cigarettes. No differences were observed in leptin levels between smokers who maintained abstinence for 4 weeks and those who relapsed during this period. These findings suggest that leptin levels may change in response to stress and that leptin could be a useful marker of craving for smoking.",
keywords = "Craving; Leptin; Relapse; Stress; Tobacco",
pubstate = "published",
tppubtype = "article"
}

@inproceedings{Srivastava2014,
title = "In Sensors We Trust--A Realistic Possibility?",
author = "M.B. Srivastava",
url = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6846138&isnumber=6846129",
year = 2014,
date = "2014-05-26",
booktitle = "2014 IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS)",
pages = "1--1",
organization = "IEEE",
abstract = "Sensors of diverse capabilities and modalities, carried by us or deeply embedded in the physical world, have invaded our personal, social, work, and urban spaces. Our relationship with these sensors is a complicated one. On the one hand, these sensors collect rich data that are shared and disseminated, often initiated by us, with a broad array of service providers, interest groups, friends, and family. Embedded in this data is information that can be used to algorithmically construct a virtual biography of our activities, revealing intimate behaviors and lifestyle patterns. On the other hand, we and the services we use, increasingly depend directly and indirectly on information originating from these sensors for making a variety of decisions, both routine and critical, in our lives. The quality of these decisions and our confidence in them depend directly on the quality of the sensory information and our trust in the sources. Sophisticated adversaries, benefiting from the same technology advances as the sensing systems, can manipulate sensory sources and analyze data in subtle ways to extract sensitive knowledge, cause erroneous inferences, and subvert decisions. The consequences of these compromises will only amplify as our society increasingly complex human-cyber-physical systems with increased reliance on sensory information and real-time decision cycles.Drawing upon examples of this two-faceted relationship with sensors in applications such as mobile health and sustainable buildings, this talk will discuss the challenges inherent in designing a sensor information flow and processing architecture that is sensitive to the concerns of both producers and consumer. For the pervasive sensing infrastructure to be trusted by both, it must be robust to active adversaries who are deceptively extracting private information, manipulating beliefs and subverting decisions. While completely solving these challenges would require a new science of resilient, secure and trustwor- hy networked sensing and decision systems that would combine hitherto disciplines of distributed embedded systems, network science, control theory, security, behavioral science, and game theory, this talk will provide some initial ideas. These include an approach to enabling privacy-utility trade-offs that balance the tension between risk of information sharing to the producer and the value of information sharing to the consumer, and method to secure systems against physical manipulation of sensed information.",
keywords = "Architecture, Buildings, Computer architecture, data mining, Information management, Security, sensors",
pubstate = "published",
tppubtype = "inproceedings"
}

@article{Mun:2014:PPD:2633905.2523820,
title = "PDVLoc: A Personal Data Vault for Controlled Location Data Sharing",
author = "M.Y. Mun and D.H. Kim and K. Shilton and D. Estrin and M. Hansen and R. Govindan",
url = "http://doi.acm.org/10.1145/2523820",
issn = "1550-4859",
year = 2014,
date = "2014-06-01",
journal = "ACM Transactions on Sensor Networks",
volume = 10,
number = 4,
pages = "58:1--58:29",
publisher = "ACM",
address = "New York, NY, USA",
abstract = "Location-Based Mobile Service (LBMS) is one of the most popular smartphone services. LBMS enables people to more easily connect with each other and analyze the aspects of their lives. However, sharing location data can leak people's privacy. We present PDVLoc, a controlled location data-sharing framework based on selectively sharing data through a Personal Data Vault (PDV). A PDV is a privacy architecture in which individuals retain ownership of their data. Data are routinely filtered before being shared with content-service providers, and users or data custodian services can participate in making controlled data-sharing decisions. Introducing PDVLoc gives users flexible and granular access control over their location data. We have implemented a prototype of PDVLoc and evaluated it using real location-sharing social networking applications, Google Latitude and Foursquare. Our user study of 19 participants over 20 days shows that most users find that PDVLoc is useful to manage and control their location data, and are willing to continue using PDVLoc.",
keywords = "Location-based mobile service, personal data vault, privacy, selective sharing, system",
pubstate = "published",
tppubtype = "article"
}

@inproceedings{Parate:2014:RRS:2594368.2594379,
title = "RisQ: Recognizing Smoking Gestures with Inertial Sensors on a Wristband",
author = "A. Parate and M. Chiu and C. Chadowitz and D. Ganesan and E. Kalogerakis",
url = "http://doi.acm.org/10.1145/2594368.2594379",
isbn = "978-1-4503-2793-0",
year = 2014,
date = "2014-06-02",
booktitle = "Proceedings of the 12th Annual International Conference on Mobile Systems, Applications, and Services (MobiSys '14)",
pages = "149--161",
publisher = "ACM",
address = "Bretton Woods, New Hampshire, USA",
series = "MobiSys '14",
abstract = "Smoking-induced diseases are known to be the leading cause of death in the United States. In this work, we design RisQ, a mobile solution that leverages a wristband containing a 9-axis inertial measurement unit to capture changes in the orientation of a person's arm, and a machine learning pipeline that processes this data to accurately detect smoking gestures and sessions in real-time. Our key innovations are four-fold: a) an arm trajectory-based method that extracts candidate hand-to-mouth gestures, b) a set of trajectory-based features to distinguish smoking gestures from confounding gestures including eating and drinking, c) a probabilistic model that analyzes sequences of hand-to-mouth gestures and infers which gestures are part of individual smoking sessions, and d) a method that leverages multiple IMUs placed on a person's body together with 3D animation of a person's arm to reduce burden of self-reports for labeled data collection. Our experiments show that our gesture recognition algorithm can detect smoking gestures with high accuracy (95.7%), precision (91%) and recall (81%). We also report a user study that demonstrates that we can accurately detect the number of smoking sessions with very few false positives over the period of a day, and that we can reliably extract the beginning and end of smoking session periods.",
keywords = "inertial measurement unit, mobile computing, smoking detection, wearables",
pubstate = "published",
tppubtype = "inproceedings"
}

@inproceedings{Mayberry:2014:IDW:2594368.2594388,
title = "iShadow: Design of a Wearable, Real-time Mobile Gaze Tracker",
author = "A. Mayberry and P. Hu and B. Marlin and C. Salthouse and D. Ganesan",
url = "http://doi.acm.org/10.1145/2594368.2594388",
isbn = "978-1-4503-2793-0",
year = 2014,
date = "2014-06-02",
booktitle = "Proceedings of the 12th Annual International Conference on Mobile Systems, Applications, and Services (MobiSys '14)",
pages = "82--94",
publisher = "ACM",
address = "Bretton Woods, New Hampshire, USA",
series = "MobiSys '14",
abstract = "Continuous, real-time tracking of eye gaze is valuable in a variety of scenarios including hands-free interaction with the physical world, detection of unsafe behaviors, leveraging visual context for advertising, life logging, and others. While eye tracking is commonly used in clinical trials and user studies, it has not bridged the gap to everyday consumer use. The challenge is that a real-time eye tracker is a power-hungry and computation-intensive device which requires continuous sensing of the eye using an imager running at many tens of frames per second, and continuous processing of the image stream using sophisticated gaze estimation algorithms. Our key contribution is the design of an eye tracker that dramatically reduces the sensing and computation needs for eye tracking, thereby achieving orders of magnitude reductions in power consumption and form-factor. The key idea is that eye images are extremely redundant, therefore we can estimate gaze by using a small subset of carefully chosen pixels per frame. We instantiate this idea in a prototype hardware platform equipped with a low-power image sensor that provides random access to pixel values, a low-power ARM Cortex M3 microcontroller, and a bluetooth radio to communicate with a mobile phone. The sparse pixel-based gaze estimation algorithm is a multi-layer neural network learned using a state-of-the-art sparsity-inducing regularization function that minimizes the gaze prediction error while simultaneously minimizing the number of pixels used. Our results show that we can operate at roughly 70mW of power, while continuously estimating eye gaze at the rate of 30 Hz with errors of roughly 3 degrees.",
keywords = "eye tracking, lifelog, neural network",
pubstate = "published",
tppubtype = "inproceedings"
}

@inproceedings{Ho2014,
title = "Poster: M-Seven: monitoring smoking event by considering time sequence information via iPhone M7 API",
author = "B. Ho and M.B. Srivastava",
url = "http://dl.acm.org/citation.cfm?id=2594368.2601451",
year = 2014,
date = "2014-06-02",
booktitle = "Proceedings of the 12th Annual International Conference on Mobile Systems, Applications, and Services (MobiSys '14)",
pages = "372--372",
organization = "ACM",
abstract = "Smartphones are equipped with various sensors that provide rich context information. By leveraging these sensors, several interesting and practical applications have emerged. Accelerometer data has been used, for example, to detect transportation, exercise activities, etc. A typical approach is to classify activity directly based on features extracted from raw sensing data. Cheng et. al. implemented a different approach by using two-stage classification: the system first detects several sub-behaviors, and uses the combination of attributes to infer higher-level behaviors. Built upon this approach, we focus on exploring the time sequence of activities, which is an underexplored, yet natural and information-rich indicator. In this work, we explore this time sequence concept through detection of smoking events.",
keywords = "accelerometer data, activity tracking, sensors, smartphones, time sequence concept",
pubstate = "published",
tppubtype = "inproceedings"
}

@article{Tamersoy2014,
title = "Large-scale insider trading analysis: patterns and discoveries",
author = "A. Tamersoy and E. Khalil and B. Xie and S.L. Lenkey and B.R. Routledge and D.H. Chau and S.B. Navathe",
url = "http://dx.doi.org/10.1007/s13278-014-0201-9",
issn = "1869-5450",
year = 2014,
date = "2014-06-08",
journal = "Social Network Analysis and Mining",
volume = 4,
number = 1,
publisher = "Springer Vienna",
abstract = "How do company insiders trade? Do their trading behaviors differ based on their roles (e.g., chief executive officer vs. chief financial officer)? Do those behaviors change over time (e.g., impacted by the 2008 market crash)? Can we identify insiders who have similar trading behaviors? And what does that tell us? This work presents the first academic, large-scale exploratory study of insider filings and related data, based on the complete Form 4 fillings from the U.S. Securities and Exchange Commission. We analyze 12 million transactions by 370 thousand insiders spanning 19862012, the largest reported in academia. We explore the temporal and network-based aspects of the trading behaviors of insiders, and make surprising and counterintuitive discoveries. We study how the trading behaviors of insiders differ based on their roles in their companies, the types of their transactions, their companies sectors, and their relationships with other insiders. Our work raises exciting research questions and opens up many opportunities for future studies. Most importantly, we believe our work could form the basis of novel tools for financial regulators and policymakers to detect illegal insider trading, help them understand the dynamics of the trades, and enable them to adapt their detection strategies toward these dynamics.",
keywords = "insider trading, networks, SEC, trading behaviors",
pubstate = "published",
tppubtype = "article"
}

@article{Shortreed2014,
title = "A multiple imputation strategy for sequential multiple assignment randomized trials",
author = "S.M. Shortreed and E. Laber and T.S. Stroup and J. Pineau and S.A. Murphy",
url = "http://www.ncbi.nlm.nih.gov/pubmed/24919867",
year = 2014,
date = "2014-06-11",
journal = "Statistics in Medicine",
volume = 33,
number = 24,
pages = "4202-14",
abstract = "Sequential multiple assignment randomized trials (SMARTs) are increasingly being used to inform clinical and intervention science. In a SMART, each patient is repeatedly randomized over time. Each randomization occurs at a critical decision point in the treatment course. These critical decision points often correspond to milestones in the disease process or other changes in a patient's health status. Thus, the timing and number of randomizations may vary across patients and depend on evolving patient-specific information. This presents unique challenges when analyzing data from a SMART in the presence of missing data. This paper presents the first comprehensive discussion of missing data issues typical of SMART studies: we describe five specific challenges and propose a flexible imputation strategy to facilitate valid statistical estimation and inference using incomplete data from a SMART. To illustrate these contributions, we consider data from the Clinical Antipsychotic Trial of Intervention and Effectiveness, one of the most well-known SMARTs to date.",
keywords = "dynamic treatment regimes; individualized treatment; missing data; multiple imputation; sequential multiple assignment randomized trials; treatment policies",
pubstate = "published",
tppubtype = "article"
}

@article{Kang2014a,
title = "A latent class analysis of cancer risk behaviors among U.S. college students",
author = "J. Kang and C.C. Ciecierski and E.L. Malin and A.J. Carroll and M. Gidea and L.L. Craft and B. Spring and B. Hitsman",
url = "http://www.sciencedirect.com/science/article/pii/S0091743514001170",
year = 2014,
date = "2014-07-01",
journal = "Elsevier Preventive Medicine",
volume = 64,
pages = "121-126",
abstract = "Objective The purpose of this study is to understand how cancer risk behaviors cluster in U.S. college students and vary by race and ethnicity. Methods Using the fall 2010 wave of the National College Health Assessment (NCHA), we conducted a latent class analysis (LCA) to evaluate the clustering of cancer risk behaviors/conditions: tobacco use, physical inactivity, unhealthy diet, alcohol binge drinking, and overweight/obesity. The identified clusters were then examined separately by students' self-reported race and ethnicity. Results Among 30,093 college students surveyed, results show a high prevalence of unhealthy diet as defined by insufficient fruit and vegetable intake (> 95%) and physical inactivity (> 60%). The LCA identified behavioral clustering for the entire sample and distinct clustering among Black and American Indian students. Conclusions Cancer risk behaviors/conditions appear to cluster among college students differentially by race. Understanding how risk behaviors cluster in young adults can lend insight to racial disparities in cancer through adulthood. Health behavior interventions focused on modifying multiple risk behaviors and tailored to students' racial group could potentially have a much larger effect on cancer prevention than those targeting any single behavior.",
keywords = "American College Health Association, Cancer risk behaviors, College students, Latent class analysis, National College Health Assessment, Racial disparities",
pubstate = "published",
tppubtype = "article"
}

@article{Bu2014,
title = "Pregelix: Big (ger) graph analytics on a dataflow engine",
author = "Y. Bu and V. Borkar and J. Jia and M.J. Carey and T. Condie",
url = "http://www.vldb.org/pvldb/vol8/p161-bu.pdf",
year = 2014,
date = "2014-07-02",
journal = "arXiv preprint arXiv:1407.0455",
abstract = "There is a growing need for distributed graph processing systems that are capable of gracefully scaling to very large graph datasets. Unfortunately, this challenge has not been easily met due to the intense memory pressure imposed by process-centric, message passing designs that many graph processing systems follow. Pregelix is a new open source distributed graph processing system that is based on an iterative dataflow design that is better tuned to handle both in-memory and out-of-core workloads. As such, Pregelix offers improved performance characteristics and scaling properties over current open source systems (e.g., we have seen up to 15× speedup compared to Apache Giraph and up to 35× speedup compared to distributed GraphLab), and more effective use of available machine resources to support Big(ger) Graph Analytics.",
keywords = "Big(ger) Graph Analytics, dataflow design, graph processing systems, Pregelix",
pubstate = "published",
tppubtype = "article"
}

@article{Collins2014,
title = "Optimization of behavioral dynamic treatment regimens based on the sequential, multiple assignment, randomized trial (SMART)",
author = "L.M. Collins and I. Nahum-Shani and D. Almirall",
url = "http://ctj.sagepub.com/content/11/4/426.long",
year = 2014,
date = "2014-07-22",
journal = "Clinical Trials",
volume = 11,
number = 4,
pages = "426-434",
publisher = "SAGE Publications",
abstract = "A behavioral intervention is a program aimed at modifying behavior for the purpose of treating or preventing disease, promoting health, and/or enhancing well-being. Many behavioral interventions are dynamic treatment regimens, that is, sequential, individualized multicomponent interventions in which the intensity and/or type of treatment is varied in response to the needs and progress of the individual participant. The multiphase optimization strategy (MOST) is a comprehensive framework for development, optimization, and evaluation of behavioral interventions, including dynamic treatment regimens. The objective of optimization is to make dynamic treatment regimens more effective, efficient, scalable, and sustainable. An important tool for optimization of dynamic treatment regimens is the sequential, multiple assignment, randomized trial (SMART). The purpose of this article is to discuss how to develop optimized dynamic treatment regimens within the MOST framework. Methods and results The article discusses the preparation, optimization, and evaluation phases of MOST. It is shown how MOST can be used to develop a dynamic treatment regimen to meet a prespecified optimization criterion. The SMART is an efficient experimental design for gathering the information needed to optimize a dynamic treatment regimen within MOST. One signature feature of the SMART is that randomization takes place at more than one point in time. Conclusion MOST and SMART can be used to develop optimized dynamic treatment regimens that will have a greater public health impact.",
keywords = "behavior, behavioral intervention, dynamic treatment regimens, MOST, multiphase optimization strategy",
pubstate = "published",
tppubtype = "article"
}

@incollection{Ertin2014,
title = "Three-Dimensional Imaging of Vehicles from Sparse Apertures in Urban Environment",
author = "E. Ertin",
url = "http://www2.ece.ohio-state.edu/~ertine/Ertin2014b.pdf",
isbn = "ISBN 9781466597846",
year = 2014,
date = "2014-08-07",
booktitle = "Compressive Sensing for Urban Radar",
journal = "Compressive Sensing for Urban Radar",
pages = 361,
publisher = "CRC Press",
abstract = "Three-dimensional synthetic aperture radar (SAR) imaging of vehicles in urban setting are made possible by new data collection capabilities, in which airborne radar systems interrogate a large scene persistently and over a large range of aspect angles. Wide-angle 3-D reconstructions of vehicles can be useful in applications such as automatic target recognition (ATR) and fingerprinting. The backscatter data collected by the airborne platform at each pulse can be interpreted as 1-D lines of the 3-D Fourier transform of the scene, and the aggregation of radar returns over the flight path defines a conical manifold of data in the scenes 3-D Fourier domain. Generating high-resolution 3-D images using traditional Fourier processing methods requires that radar data be collected over a densely sampled set of points in both azimuth and elevation angle. This method of imaging requires very large collection times and storage requirements and may be prohibitively costly in practice. There is thus moti- vation to consider more sparsely sampled data collection strategies, where only a small fraction of the data required to perform traditional high-resolution imaging is collected. In this chapter, we review several techniques that have been proposed for 3-D reconstruction data collected from sparsely apertures, as well as discuss new approaches based on dictionary learning of target primitives. Particular emphasis is given sparsity regularized least squares approaches to wide-angle 3-D radar reconstruction for arbitrary, sparse apertures. We provide comprehensive set of comparative results using data from the GOTCHA data collection campaign.",
keywords = "airborne radar systems, automatic target recognition, Fourier processing, Synthetic aperture radar",
pubstate = "published",
tppubtype = "incollection"
}

@article{Abraham2014,
title = "Trials of implantable monitoring devices in heart failure: which design is optimal?",
author = "W.T. Abraham and W.G. Stough and I.L. Pina and C. Linde and J.S. Borer and G.M. De Ferrari and R. Mehran and K.M. Stein and A. Vincent and J.S. Yadav and S.D. Anker and F. Zannad",
url = "http://www.nature.com/nrcardio/journal/v11/n10/full/nrcardio.2014.114.html",
year = 2014,
date = "2014-08-12",
journal = "Nature Reviews Cardiology",
volume = 11,
number = 10,
pages = "576--585",
publisher = "Nature Publishing Group",
abstract = "Implantable monitoring devices have been developed to detect early evidence of heart failure (HF) decompensation, with the hypothesis that early detection might enable clinicians to commence therapy sooner than would otherwise be possible, and potentially to reduce the rate of hospitalization. In addition to the usual challenges inherent to device trials (such as the difficulty of double-blinding and potential for bias), studies of implantable monitoring devices present unique difficulties because they involve assessment of therapeutic end points for diagnostic devices. Problems include the lack of uniform approaches to treatment in study protocols for device alerts or out-of-range values, and the requirement of levels of evidence traditionally associated with therapeutic devices to establish effectiveness and safety. In this Review, the approaches used to deal with these issues are discussed, including the use of objective primary end points with blinded adjudication, identical duration of follow-up and number of encounters for patients in active monitoring and control groups, and treatment recommendations between groups that are consistent with international guidelines. Remote monitoring devices hold promise for reducing the rate of hospitalization among patients with HF. However, optimization of regulatory approaches and clinical trial design is needed to facilitate further evaluation of the effectiveness of combining health information technology and medical devices.",
keywords = "Heart failure, hospitalization, implantable monitor",
pubstate = "published",
tppubtype = "article"
}

@article{laber2014,
title = "Dynamic treatment regimes: Technical challenges and applications",
author = "E. Laber and D.J. LIzotte and M. Qian and W.E. Pelman and S.A. Murphy",
url = "http://projecteuclid.org/euclid.ejs/1408540283",
year = 2014,
date = "2014-08-20",
journal = "Electronic Journal of Statistics",
volume = 8,
number = 1,
pages = "1225--1272",
abstract = "Dynamic treatment regimes are of growing interest across the clinical sciences because these regimes provide one way to operationalize and thus inform sequential personalized clinical decision making. Formally, a dynamic treatment regime is a sequence of decision rules, one per stage of clinical intervention. Each decision rule maps up-to-date patient information to a recommended treatment. We briefly review a variety of approaches for using data to construct the decision rules. We then review a critical inferential challenge that results from nonregularity, which often arises in this area. In particular, nonregularity arises in inference for parameters in the optimal dynamic treatment regime; the asymptotic, limiting, distribution of estimators are sensitive to local perturbations. We propose and evaluate a locally consistent Adaptive Confidence Interval (ACI) for the parameters of the optimal dynamic treatment regime. We use data from the Adaptive Pharmacological and Behavioral Treatments for Children with ADHD Trial as an illustrative example. We conclude by highlighting and discussing emerging theoretical problems in this area.",
keywords = "ADHD, dynamic treatment regimes, patient information, sequential personalized clinical decision-making",
pubstate = "published",
tppubtype = "article"
}

@inproceedings{Tamersoy:2014:GAL:2623330.2623342,
title = "Guilt by Association: Large Scale Malware Detection by Mining File-relation Graphs",
author = "A. Tamersoy and K. Roundy and D.H. Chau",
url = "http://doi.acm.org/10.1145/2623330.2623342",
isbn = "978-1-4503-2956-9",
year = 2014,
date = "2014-08-24",
booktitle = "Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",
pages = "1524--1533",
publisher = "ACM",
address = "New York, New York, USA",
series = "KDD '14",
abstract = {The increasing sophistication of malicious software calls for new defensive techniques that are harder to evade, and are capable of protecting users against novel threats. We present AESOP, a scalable algorithm that identifies malicious executable files by applying Aesop's moral that "a man is known by the company he keeps." We use a large dataset voluntarily contributed by the members of Norton Community Watch, consisting of partial lists of the files that exist on their machines, to identify close relationships between files that often appear together on machines. AESOP leverages locality-sensitive hashing to measure the strength of these inter-file relationships to construct a graph, on which it performs large scale inference by propagating information from the labeled files (as benign or malicious) to the preponderance of unlabeled files. AESOP attained early labeling of 99% of benign files and 79% of malicious files, over a week before they are labeled by the state-of-the-art techniques, with a 0.9961 true positive rate at flagging malware, at 0.0001 false positive rate.},
keywords = "belief propagation, file graph, graph mining, locality sensitive hashing, malware detection",
pubstate = "published",
tppubtype = "inproceedings"
}

@article{malhotra2014,
title = "Temporal Event Sequence Mining for Glioblastoma Survival Prediction",
author = "K. Malhotra and D.H. Chau and J. Sun and C. Hadjipanayis and S.B. Navath",
url = "http://cci.drexel.edu/HI-KDD2014/morning_1.pdf",
year = 2014,
date = "2014-08-24",
journal = "ACM SIGKDD '14 Workshop on Health Informatics (HI-KDD 2014)",
abstract = "One of the many challenges in the field of medicine is to make the best decisions about optimal treatment plans for patients. Medical practitioners often have differing opinions about the best treatment among multiple available options. While standard protocols are in place for the first and second lines of treatment for most diseases, a lot of variation exists in the treatment plans subsequently chosen. As a representative disease we study Glioblastoma Multiforme (GBM) which is a rare form of brain tumor. The goal of our study is to predict patients surviving for greater than the median survival period for GBM and discover in addition to clinical and genomic factors, certain treatment patterns which influence longevity. We use publicly available data for 300 patients spanning a period of 2 years from The Cancer Genome Atlas Portal, which has actual de-identified patient data from multiple institutions. Information about each patient comprises a set of features from the clinical and the genomic domain. We also use sequential mining algorithms to extract treatment patterns and use the patterns themselves as additional features. A model predicting whether a patient would survive for more than a year is developed using logistic regression and the most predictive features influencing the survival period of GBM patients include mRNA expression levels of certain genes and medications given in a particular sequence. The model achieved an AUC of 0.85 with an accuracy of 86.4% .The study is a preliminary step in a long term plan of developing personalized treatment plans with GBM patients as an initial model that can later be extended to other diseases.",
keywords = "Predictive Modeling, Sequential Pattern Mining",
pubstate = "published",
tppubtype = "article"
}

@article{DePasse2014173,
title = "Academic Medical Centers as Digital Health Catalysts",
author = "J.W. DePasse and A. Sawyer and C.E. Chen and K. Jethwani and I. Sim",
url = "http://www.sciencedirect.com/science/article/pii/S2213076414000554",
year = 2014,
date = "2014-09-01",
journal = "Elsevier Healthcare",
volume = 2,
number = 3,
pages = "173-176",
abstract = "Emerging digital technologies offer enormous potential to improve quality, reduce cost, and increase patient-centeredness in healthcare. Academic Medical Centers (AMCs) play a key role in advancing medical care through cutting-edge medical research, yet traditional models for invention, validation and commercialization at AMCs have been designed around biomedical initiatives, and are less well suited for new digital health technologies. Recently, two large bi-coastal Academic Medical Centers, the University of California, San Francisco (UCSF) through the Center for Digital Health Innovation (CDHI) and Partners Healthcare through the Center for Connected Health (CCH) have launched centers focused on digital health innovation. These centers show great promise but are also subject to significant financial, organizational, and visionary challenges. We explore these AMC initiatives, which share the following characteristics: a focus on academic research methodology; integration of digital technology in educational programming; evolving models to support clinician innovators; strategic academicindustry collaboration and emergence of novel revenue models.",
keywords = "Academic Medical Centers, ACO, Digital health, Health IT, Innovation",
pubstate = "published",
tppubtype = "article"
}

D Almirall, I Nahum-Shani, N E Sherwood and S A Murphy. Introduction to SMART designs for the development of adaptive interventions: with application to weight loss research. Translational Behavioral Medicine 4(3):260–274, 2014. URLBibTeX

@article{almirall2014introduction,
title = "Introduction to SMART designs for the development of adaptive interventions: with application to weight loss research",
author = "D. Almirall and I. Nahum-Shani and N.E. Sherwood and S.A. Murphy",
url = "http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4167891/pdf/13142_2014_Article_265.pdf",
year = 2014,
date = "2014-09-01",
journal = "Translational Behavioral Medicine",
volume = 4,
number = 3,
pages = "260--274",
publisher = "Springer",
abstract = "The management of many health disorders often entails a sequential, individualized approach whereby treatment is adapted and readapted over time in response to the specific needs and evolving status of the individual. Adaptive interventions provide one way to operationalize the strategies (e.g., continue, augment, switch, step-down) leading to individualized sequences of treatment. Often, a wide variety of critical questions must be answered when developing a high-quality adaptive intervention. Yet, there is often insufficient empirical evidence or theoretical basis to address these questions. The Sequential Multiple Assignment Randomized Trial (SMART)—a type of research design—was developed explicitly for the purpose of building optimal adaptive interventions by providing answers to such questions. Despite increasing popularity, SMARTs remain relatively new to intervention scientists. This manuscript provides an introduction to adaptive interventions and SMARTs. We discuss SMART design considerations, including common primary and secondary aims. For illustration, we discuss the development of an adaptive intervention for optimizing weight loss among adult individuals who are overweight.",
keywords = "Adaptive treatment strategies, Dynamic treatment regimens or regimes, Experimental design, Individualized or personalized behavioral interventions, Timing and sequencing of intervention components",
pubstate = "published",
tppubtype = "article"
}

@incollection{Ciptadi2014,
title = "Movement Pattern Histogram for Action Recognition and Retrieval",
author = "A. Ciptadi and M.S. Goodwin and J.M. Rehg",
url = "http://www.cc.gatech.edu/~aciptadi/ciptadi-eccv2014.pdf",
year = 2014,
date = "2014-09-06",
booktitle = "2014 European Conference on Computer Vision (ECCV 2014)",
pages = "695--710",
publisher = "Springer International Publishing",
abstract = "We present a novel action representation based on encoding the global temporal movement of an action. We represent an action as a set of movement pattern histograms that encode the global temporal dynamics of an action. Our key observation is that temporal dynamics of an action are robust to variations in appearance and viewpoint changes, making it useful for action recognition and retrieval. We pose the problem of computing similarity between action representations as a maximum matching problem in a bipartite graph. We demonstrate the effectiveness of our method for cross-view action recognition on the IXMAS dataset. We also show how our representation complements existing bag-of-features representations on the UCF50 dataset. Finally we show the power of our representation for action retrieval on a new real-world dataset containing repetitive motor movements emitted by children with autism in an unconstrained classroom setting.",
keywords = "Autism Spectrum Disorder, IXMAS dataset, temporal movement, UCF50 dataset",
pubstate = "published",
tppubtype = "incollection"
}

@inproceedings{Hu2014,
title = "Leveraging interleaved signal edges for concurrent backscatter",
author = "P. Hu and P. Zhang and D. Ganesan",
url = "http://dl.acm.org/citation.cfm?id=2643617",
year = 2014,
date = "2014-09-11",
booktitle = "Proceedings of the 1st ACM workshop on Hot topics in wireless",
pages = "13--18",
organization = "ACM",
abstract = {One of the central challenges in backscatter is how to enable concurrent transmissions. Most backscatter protocols operate in a sequential TDMA-like manner due to the fact that most nodes cannot overhear each other’s transmissions, which is detrimental for throughout and energy consumption. Recent e↵orts to separate concurrent signals by inverting a system of linear equations is also problematic due to varying channel coe"cients caused by system and environmental dynamics. In this paper, we introduce BST, a novel physical layer for backscatter communication that enables concurrent transmission by leveraging intra-bit multiplexing of OOK signals from multiple tags. The key idea underlying BST is that the reader can sample at considerably higher rates than the tags, hence it can extract time-domain signal edges that result from interleaved transmissions of several tags. Our preliminary experiment results show that BST can achieve 5⇥ the throughput of Buzz and 10⇥ the throughput of TDMA-based solutions, such as EPC Gen 2.},
keywords = "backscatter, Concurrent, Signal Processing",
pubstate = "published",
tppubtype = "inproceedings"
}

H Sarker, M Sharmin, A A Ali, M M Rahman, R Bari, S M Hossain and S Kuma. Assessing the Availability of Users to Engage in Just-in-time Intervention in the Natural Environment. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing. 2014, 909–920. URLBibTeX

@inproceedings{Sarker:2014:AAU:2632048.2636082,
title = "Assessing the Availability of Users to Engage in Just-in-time Intervention in the Natural Environment",
author = "H. Sarker and M. Sharmin and A.A. Ali and M.M. Rahman and R. Bari and S.M. Hossain and S. Kuma",
url = "http://doi.acm.org/10.1145/2632048.2636082",
isbn = "978-1-4503-2968-2",
year = 2014,
date = "2014-09-13",
booktitle = "Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing",
pages = "909--920",
publisher = "ACM",
address = "Seattle, Washington",
series = "UbiComp '14",
abstract = "Wearable wireless sensors for health monitoring are enabling the design and delivery of just-in-time interventions (JITI). Critical to the success of JITI is to time its delivery so that the user is available to be engaged. We take a first step in modeling users' availability by analyzing 2,064 hours of physiological sensor data and 2,717 self-reports collected from 30 participants in a week-long field study. We use delay in responding to a prompt to objectively measure availability. We compute 99 features and identify 30 as most discriminating to train a machine learning model for predicting availability. We find that location, affect, activity type, stress, time, and day of the week, play significant roles in predicting availability. We find that users are least available at work and during driving, and most available when walking outside. Our model finally achieves an accuracy of 74.7% in 10-fold cross-validation and 77.9% with leave-one-subject-out.",
keywords = "EMA, interruption, intervention, mobile application, mobile health, self-report",
pubstate = "published",
tppubtype = "inproceedings"
}

J Hernandez, J Riobo, A Rozga, G D Abowd and R W Picard. Using electrodermal activity to recognize ease of engagement in children during social interactions. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2014). 2014, 307–317. URLBibTeX

@inproceedings{Hernandez2014,
title = "Using electrodermal activity to recognize ease of engagement in children during social interactions",
author = "J. Hernandez and J. Riobo and A. Rozga and G.D. Abowd and R.W. Picard",
url = "http://dl.acm.org/citation.cfm?id=2636065",
year = 2014,
date = "2014-09-13",
booktitle = "Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2014)",
pages = "307--317",
organization = "ACM",
abstract = "The recent emergence of comfortable wearable sensors has focused almost entirely on monitoring physical activity, ignoring opportunities to monitor more subtle phenomena, such as the quality of social interactions. We argue that it is compelling to address whether physiological sensors can shed light on quality of social interactive behavior. This work leverages the use of a wearable electrodermal activity (EDA) sensor to recognize ease of engagement of children during a social interaction with an adult. In particular, we monitored 51 child-adult dyads in a semistructured play interaction and used Support Vector Machines to automatically identify children who had been rated by the adult as more or less difficult to engage. We report on the classification value of several features extracted from the child's EDA responses, as well as several other features capturing the physiological synchrony between the child and the adult.",
keywords = "Electrodermal Activity, feature analysis, physiology, social engagement, Support Vector Machines.",
pubstate = "published",
tppubtype = "inproceedings"
}

@inproceedings{Park2014,
title = "Learning to reach into the unknown: Selecting initial conditions when reaching in clutter",
author = "D. Park and A. Kapusta and Y.K. Kim and J.M. Rehg and C.C. Kemp",
url = "http://www.hsi.gatech.edu/hrl/pdf/iros2014_lic.pdf",
year = 2014,
date = "2014-09-14",
booktitle = "2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014)",
pages = "630--637",
organization = "IEEE",
abstract = "Often in highly-cluttered environments, a robot can observe the exterior of the environment with ease, but cannot directly view nor easily infer its detailed internal structure (e.g., dense foliage or a full refrigerator shelf). We present a data-driven approach that greatly improves a robots success at reaching to a goal location in the unknown interior of an environment based on observable external properties, such as the category of the clutter and the locations of openings into the clutter (i.e., apertures). We focus on the problem of selecting a good initial configuration for a manipulator when reaching with a greedy controller. We use density estimation to model the probability of a successful reach given an initial condition and then perform constrained optimization to find an initial condition with the highest estimated probability of success. We evaluate our approach with two simulated robots reaching in clutter, and provide a demonstration with a real PR2 robot reaching to locations through random apertures. In our evaluations, our approach significantly outperformed two alternative approaches when making two consecutive reach attempts to goals in distinct categories of unknown clutter. Notably, our approach only uses sparse readily-apparent features.",
keywords = "density estimation, environmental clutter, robotics",
pubstate = "published",
tppubtype = "inproceedings"
}

@incollection{Kundu2014,
title = "Joint Semantic Segmentation and 3D Reconstruction from Monocular Video",
author = "A. Kundu and Y. Li and F. Dellaert and F. Li and J.M. Rehg",
url = "http://www.cc.gatech.edu/~dellaert/pubs/Kundu14eccv.pdf http://link.springer.com/chapter/10.1007/978-3-319-10599-4_45",
year = 2014,
date = "2014-09-16",
booktitle = "Proceedings of the 2014 European Conference on Computer Vision (ECCV )",
pages = "703--718",
publisher = "Springer International Publishing",
abstract = "We present an approach for joint inference of 3D scene structure and semantic labeling for monocular video. Starting with monocular image stream, our framework produces a 3D volumetric semantic + occupancy map, which is much more useful than a series of 2D semantic label images or a sparse point cloud produced by traditional semantic segmentation and Structure from Motion(SfM) pipelines respectively. We derive a Conditional Random Field (CRF) model defined in the 3D space, that jointly infers the semantic category and occupancy for each voxel. Such a joint inference in the 3D CRF paves the way for more informed priors and constraints, which is otherwise not possible if solved separately in their traditional frameworks. We make use of class specific semantic cues that constrain the 3D structure in areas, where multiview constraints are weak. Our model comprises of higher order factors, which helps when the depth is unobservable. We also make use of class specific semantic cues to reduce either the degree of such higher order factors, or to approximately model them with unaries if possible. We demonstrate improved 3D structure and temporally consistent semantic segmentation for difficult, large scale, forward moving monocular image sequences.",
keywords = "3D scene structure, conditional random field model, monocular video, semantic labeling",
pubstate = "published",
tppubtype = "incollection"
}

@inproceedings{Vhaduri:2014:EDS:2667317.2667335,
title = "Estimating Drivers' Stress from GPS Traces",
author = "S. Vhaduri and A.A. Ali and M. Sharmin and K. Hovsepian and S. Kumar",
url = "http://doi.acm.org/10.1145/2667317.2667335",
isbn = "978-1-4503-3212-5",
year = 2014,
date = "2014-09-17",
booktitle = "Proceedings of the 6th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI '14)",
pages = "20:1--20:8",
publisher = "ACM",
address = "Seattle, WA, USA",
series = "AutomotiveUI '14",
abstract = {Driving is known to be a daily stressor. Measurement of driver's stress in real-time can enable better stress management by increasing self-awareness. Recent advances in sensing technology has made it feasible to continuously assess driver's stress in real-time, but it requires equipping the driver with these sensors and/or instrumenting the car. In this paper, we present "GStress", a model to estimate driver's stress using only smartphone GPS traces. The GStress model is developed and evaluated from data collected in a mobile health user study where 10 participants wore physiological sensors for 7 days (for an average of 10.45 hours/day) in their natural environment. Each participant engaged in 10 or more driving episodes, resulting in a total of 37 hours of driving data. We find that major driving events such as stops, turns, and braking increase stress of the driver. We quantify their impact on stress and thus construct our GStress model by training a Generalized Linear Mixed Model (GLMM) on our data. We evaluate the applicability of GStress in predicting stress from GPS traces, and obtain a correlation of 0.72. By obviating any burden on the driver or the car, we believe, GStress can make driver's stress assessment ubiquitous.},
keywords = "Driving, GPS, mobile health, Stress",
pubstate = "published",
tppubtype = "inproceedings"
}

@article{Bhattacharjee2014,
title = "Inferring Object Properties from Incidental Contact with a Tactile Sensing Forearm",
author = "T. Bhattacharjee and J.M. Rehg and C.C. Kemp",
url = "http://arxiv.org/abs/1409.4972",
year = 2014,
date = "2014-09-17",
journal = "arXiv.org (preprint arXiv:1409.4972)",
abstract = "Whole-arm tactile sensing enables a robot to sense properties of contact across its entire arm. By using this large sensing area, a robot has the potential to acquire useful information from incidental contact that occurs while performing a task. Within this paper, we demonstrate that data-driven methods can be used to infer mechanical properties of objects from incidental contact with a robot's forearm. We collected data from a tactile-sensing forearm as it made contact with various objects during a simple reaching motion. We then used hidden Markov models (HMMs) to infer two object properties (rigid vs. soft and fixed vs. movable) based on low-dimensional features of time-varying tactile sensor data (maximum force, contact area, and contact motion). A key issue is the extent to which data-driven methods can generalize to robot actions that differ from those used during training. To investigate this issue, we developed an idealized mechanical model of a robot with a compliant joint making contact with an object. This model provides intuition for the classification problem. We also conducted tests in which we varied the robot arm's velocity and joint stiffness. We found that, in contrast to our previous methods [1], multivariate HMMs achieved high cross-validation accuracy and successfully generalized what they had learned to new robot motions with distinct velocities and joint stiffnesses.",
keywords = "Classification., Haptics, Hidden Markov Models, Tactile Sensing",
pubstate = "published",
tppubtype = "article"
}

@article{Kilbourne2014,
title = "Protocol: Adaptive Implementation of Effective Programs Trial (ADEPT): cluster randomized SMART trial comparing a standard versus enhanced implementation strategy to improve outcomes of a mood disorders program",
author = "A.M. Kilbourne and D. Almirall and D. Eisenberg and J. Waxmonsky and D.E. Goodrich and J.C. Fortney and J.E. Kirchner and L.I. Solberg and D. Main and M.S. Bauer and J. Kyle and S.A. Murphy and J.M. Nord and M.R. Thomas",
url = "http://www.implementationscience.com/content/pdf/s13012-014-0132-x.pdf",
year = 2014,
date = "2014-09-30",
journal = "Implementation Science",
volume = 9,
number = 1,
pages = 132,
publisher = "BioMed Central Ltd",
abstract = "Background: Despite the availability of psychosocial evidence-based practices (EBPs), treatment and outcomes for persons with mental disorders remain suboptimal. Replicating Effective Programs (REP), an effective implementation strategy, still resulted in less than half of sites using an EBP. The primary aim of this cluster randomized trial is to determine, among sites not initially responding to REP, the effect of adaptive implementation strategies that begin with an External Facilitator (EF) or with an External Facilitator plus an Internal Facilitator (IF) on improved EBP use and patient outcomes in 12 months. Methods/Design: This study employs a sequential multiple assignment randomized trial (SMART) design to build an adaptive implementation strategy. The EBP to be implemented is life goals (LG) for patients with mood disorders across 80 community-based outpatient clinics (N = 1,600 patients) from different U.S. regions. Sites not initially responding to REP (defined as <50% patients receiving ≥3 EBP sessions) will be randomized to receive additional support from an EF or both EF/IF. Additionally, sites randomized to EF and still not responsive will be randomized to continue with EF alone or to receive EF/IF. The EF provides technical expertise in adapting LG in routine practice, whereas the on-site IF has direct reporting relationships to site leadership to support LG use in routine practice. The primary outcome is mental health-related quality of life; secondary outcomes include receipt of LG sessions, mood symptoms, implementation costs, and organizational change. Diiscussion: This study design will determine whether an off-site EF alone versus the addition of an on-site IF improves EBP uptake and patient outcomes among sites that do not respond initially to REP. It will also examine the value of delaying the provision of EF/IF for sites that continue to not respond despite EF.",
keywords = "Adaptive intervention, Care management, Depression, Health behavior change",
pubstate = "published",
tppubtype = "article"
}

@inproceedings{187114,
title = "ViRUS: Virtual Function Replacement Under Stress",
author = "L. Wanner and M.B. Srivastava",
url = "http://blogs.usenix.org/conference/hotpower14/workshop-program/presentation/wanner",
year = 2014,
date = "2014-10-01",
booktitle = "6th Workshop on Power-Aware Computing and Systems (HotPower 14)",
publisher = "USENIX Association",
address = "Broomfield, CO",
abstract = "In this paper we introduce ViRUS: Virtual function Replacement Under Stress. ViRUS allows the runtime system to switch between blocks of code that perform equivalent functionality at different Quality-of- Service levels when the system is under stress  be it in the form of scarce energy resources, temperature emergencies, or various sources of environmental and process variability  with the ultimate goal of energy efficiency. We demonstrate ViRUS with a framework for transparent function replacement in shared libraries and a polymorphic version of the standard C math library in Linux. Case studies show how ViRUS can tradeoff upwards of 4% degradation in application quality for a band of upwards of 50% savings in energy consumption.",
keywords = "energy consumption, system stress levels, VIRUS",
pubstate = "published",
tppubtype = "inproceedings"
}

@article{Roche2014,
title = "Enriching psychological assessment using a person-specific analysis of interpersonal processes in daily life",
author = "M.J. Roche and A.L. Pincus and A.L. Rebar and D.E. Conroy and N. Ram",
url = "http://asm.sagepub.com/content/21/5/515.full.pdf+html",
year = 2014,
date = "2014-10-01",
journal = "Assessment",
pages = 1073191114540320,
publisher = "SAGE Publications",
abstract = "We present a series of methods and approaches for clinicians interested in tracking their individual patients over time and in the natural settings of their daily lives. The application of person-specific analyses to intensive repeated measurement data can assess some aspects of persons that are distinct from the valuable results obtained from single-occasion assessments. Guided by interpersonal theory, we assess a psychotherapy patients interpersonal processes as they unfold in his daily life. We highlight specific contexts that change these processes, use an informant report to examine discrepancies in his reported interpersonal processes, and examine how his interpersonal processes differ as a function of varying levels of self-esteem and anger. We advocate for this approach to complement existing psychological assessments and provide a scoring program to facilitate initial implementation.",
keywords = "intraindividual variation personality assessment interpersonal complementarity intensive repeated measures in natural settings ecological momentary assessment event contingent recording p-technique",
pubstate = "published",
tppubtype = "article"
}

@article{Lagoa2014,
title = "Designing adaptive intensive interventions using methods from engineering.",
author = "C.M. Lagoa and K. Bekiroglu and S.T. Lanza and S.A. Murphy",
url = "http://psycnet.apa.org/journals/ccp/82/5/868/",
year = 2014,
date = "2014-10-01",
journal = "Journal of Consulting and Clinical Psychology",
volume = 82,
number = 5,
pages = 868,
publisher = "American Psychological Association",
abstract = "OBJECTIVE: Adaptive intensive interventions are introduced, and new methods from the field of control engineering for use in their design are illustrated. METHOD: A detailed step-by-step explanation of how control engineering methods can be used with intensive longitudinal data to design an adaptive intensive intervention is provided. The methods are evaluated via simulation. RESULTS: Simulation results illustrate how the designed adaptive intensive intervention can result in improved outcomes with less treatment by providing treatment only when it is needed. Furthermore, the methods are robust to model misspecification as well as the influence of unobserved causes. CONCLUSIONS: These new methods can be used to design adaptive interventions that are effective yet reduce participant burden.",
keywords = "adaptive interventions, control engineering",
pubstate = "published",
tppubtype = "article"
}

@inproceedings{Elmalaki2014,
title = "A case for battery charging-aware power management and deferrable task scheduling in smartphones",
author = "S. Elmalaki and M. Gottscho and P. Gupta and M.B. Srivastava",
url = "https://www.usenix.org/conference/hotpower14/workshop-program/presentation/elmalaki",
year = 2014,
date = "2014-10-01",
booktitle = "Proceedings of the 6th USENIX conference on Power-Aware Computing and Systems",
pages = "4--4",
address = "Broomfield, CO",
organization = "USENIX Association",
abstract = "Prior battery-aware systems research has focused on discharge power management in order to maximize the usable battery lifetime of a device. In order to achieve the vision of perpetual mobile device operation, we propose that software also needs to carefully consider the process of battery charging. This is because the power consumed by the system when plugged in can influence the rate of battery charging, and hence, the availability of the system to the user. We characterize the charging process of a Nexus 4 smartphone and analyze the charging behaviors of anonymous Nexus 4 users using the Device Analyzer dataset. We find that there is potential for software schedulers to increase device availability by distributing tasks across the charging period. We estimate that approximately 53% of the users we examined could benefit from up to 18.9% improvement in net energy gained by the battery while charging. Accordingly, we propose new threads of research in charging-aware power management and deferrable task scheduling that could improve overall availability for a significant portion of smartphone users.",
keywords = "batteries, battery lifetime, discharge power management, mobile devices, smartphone users, smartphones",
pubstate = "published",
tppubtype = "inproceedings"
}

@article{Adamson2014a,
title = "Wireless Pulmonary Artery Pressure Monitoring Guides Management to Reduce Decompensation in Heart Failure With Preserved Ejection Fraction",
author = "P.B. Adamson and W.T. Abraham and R.C. Bourge and M.R. Costanzo and A. Hasan and C. Yadav and J. Henderson and P. Cowart and L.W. Stevenson",
url = "http://circheartfailure.ahajournals.org/content/early/2014/10/06/CIRCHEARTFAILURE.113.001229.abstract",
year = 2014,
date = "2014-10-06",
journal = "Circulation: Heart Failure",
pages = "CIRCHEARTFAILURE--113",
publisher = "Lippincott Williams & Wilkins",
abstract = "Background—No treatment strategies have been demonstrated to be beneficial for the population for patients with heart failure and preserved ejection fraction. Methods and Results—The CHAMPION Trial was a prospective, single-blinded, randomized controlled clinical trial testing the hypothesis that hemodynamically guided heart failure management decreases decompensation leading to hospitalization. Of the 550 patients enrolled in the study, 119 had LVEF ≥40% (average 50.6%), 430 patients had low LVEF (<40%, average 23.3%) and one patient had no documented LVEF. A microelectromechanical systems (MEMS) pressure sensor was permanently implanted in all participants during right heart catheterization. After implant, subjects were randomly assigned in single-blind fashion to a treatment group in whom daily uploaded pressures were used in a treatment strategy for HF management or to a control group in whom standard HF management included weight-monitoring , and pressures were uploaded but not available for investigator use. The primary efficacy endpoint of HF hospitalization rate over 6 months for preserved EF patients was 46% lower in the treatment group compared to control (IRR 0.54, C.I. 0.38-0.70, p<0.0001). After an average of 17.6 months of blinded follow-up, the hospitalization rate was 50% lower (IRR 0.50, C.I. 0.35-0.70, p<0.0001). In response to PA pressure information, more changes in diuretic and vasodilator therapies were made in the treatment group. Conclusions—Hemodynamically-guided management of HF patients with preserved ejection fraction reduced decompensation leading to hospitalization compared to standard HF management strategies.",
keywords = "heart failure with preserved ejection fraction, hemodynamic monitoring, hospitalization",
pubstate = "published",
tppubtype = "article"
}

@article{Lam2014,
title = "Individual and Combined Effects of Multiple High-Risk Triggers on Postcessation Smoking Urge and Lapse",
author = "C.Y. Lam and M.S. Businelle and C.J. Aigner and J.B. McClure and L. Cofta-Woerpel and P.M. Cinciripini and D.W. Wetter",
url = "http://ntr.oxfordjournals.org/content/16/5/569.short",
year = 2014,
date = "2014-10-15",
journal = "Nicotine and Tobacco Research",
volume = 16,
number = 5,
pages = "569-575",
abstract = "Introduction: Negative affect, alcohol consumption, and presence of others smoking have consistently been implicated as risk factors for smoking lapse and relapse. What is not known, however, is how these factors work together to affect smoking outcomes. This paper uses ecological momentary assessment (EMA) collected during the first 7 days of a smoking cessation attempt to test the individual and combined effects of high-risk triggers on smoking urge and lapse. Methods: Participants were 300 female smokers who enrolled in a study that tested an individually tailored smoking cessation treatment. Participants completed EMA, which recorded negative affect, alcohol consumption, presence of others smoking, smoking urge, and smoking lapse, for 7 days starting on their quit date. Results: Alcohol consumption, presence of others smoking, and negative affect were, independently and in combination, associated with increase in smoking urge and lapse. The results also found that the relationship between presence of others smoking and lapse and the relationship between negative affect and lapse were moderated by smoking urge. Conclusions: The current study found significant individual effects of alcohol consumption, presence of other smoking, and negative affect on smoking urge and lapse. Combing the triggers increased smoking urge and the risk for lapse to varying degrees, and the presence of all 3 triggers resulted in the highest urge and lapse risk.",
keywords = "cessation, smoking, smoking cessation, tobacco, triggers",
pubstate = "published",
tppubtype = "article"
}

@article{Jakicic2014,
title = "Comparative Effectiveness Research: A Roadmap for Physical Activity and Lifestyle.",
author = "J.M. Jakicic and H. Sox and S.N. Blair and M. Bensink and W.G. Johnson and A.C. King and I. Lee and I. Nahum-Shani and J.F. Sallis and R.E. Sallis and L. Craft and J.R. Whitehead and B.E. Ainsworth",
url = "http://dx.doi.org/10.1249/MSS.0000000000000590",
year = 2014,
date = "2014-10-20",
journal = "Medicine & Science in Sports & Exercise",
institution = "1University of Pittsburgh, Pittsburgh, PA; 2Dartmouth College, Hanover, NH; 3University of South Carolina, Columbia, SC; 4Fred Hutchinson Cancer Research Center, Seattle, WA; 5Arizona State University",
abstract = "Comparative Effectiveness Research (CER) is designed to support informed decision making at both the individual, population, and policy levels. The American College of Sports Medicine and partners convened a conference with the focus of building an agenda for CER within the context of physical activity and non-pharmacological lifestyle approaches in the prevention and treatment of chronic disease. This report summarizes the conference content and consensus recommendations that culminated in a CER Roadmap for Physical Activity and Lifestyle approaches to reducing the risk of chronic disease.This conference focused on presentations and discussion around the following topic areas: 1) defining CER, 2) identifying the current funding climate to support CER, 3) summarizing methods for conducting CER, and 4) identifying CER opportunities for physical activity.This conference resulted in consensus recommendations to adopt a CER Roadmap for Physical Activity and Lifestyle approaches to reducing the risk of chronic disease. In general, this roadmap provides a systematic framework by which CER for physical activity can move from a planning phase, to a phase of engagement in CER related to lifestyle factors with particular emphasis on physical activity, to a societal change phase that results in changes in policy, practice, and health.It is recommended that physical activity researchers and healthcare providers use the roadmap developed from this conference as a method to systematically engage in and apply CER to the promotion of physical activity as a key lifestyle behavior that can be effective at impacting a variety of health-related outcomes.",
keywords = "CER, chronic disease, Exercise, prevention, treatment",
pubstate = "published",
tppubtype = "article"
}

@article{Carlson2014,
title = "Validity of PALMS GPS Scoring of Active and Passive Travel Compared to SenseCam.",
author = "J.A. Carlson and M.M. Jankowska and K. Meseck and S. Godbole and L. Natarajan and F. Raab and B. Demchak and K. Patrick and J. Kerr",
url = "http://www.researchgate.net/publication/263814943_Validity_of_PALMS_GPS_Scoring_of_Active_and_Passive_Travel_Compared_to_SenseCam",
year = 2014,
date = "2014-10-23",
journal = "Medicine & Science in Sports & Exercise",
abstract = "Purpose: The objective of this study is to assess validity of the personal activity location measurement system (PALMS) for deriving time spent walking/running, bicycling, and in vehicle, using SenseCam as the comparison. Methods: AQ1 Forty adult cyclists wore a Qstarz BT-Q1000XT GPS data logger and SenseCam (camera worn around the neck capturing multiple images every minute) for a mean time of 4 d. PALMS used distance and speed between global positioning system (GPS) points to classify whether each minute was part of a trip (yes/no), and if so, the trip mode (walking/running, bicycling, or in vehicle). SenseCam images were annotated to create the same classifications (i.e., trip yes/no and mode). Contingency tables (2 2) and confusion matrices were calculated at the minute level for PALMS versus SenseCam classifications. Mixed-effects linear regression models estimated agreement (mean differences and intraclass correlation coefficients) between PALMS and SenseCam with regard to minutes/day in each mode. Results: Minute-level sensitivity, specificity, and negative predictive value were Q88%, and positive predictive value was Q75% for non–mode-specific trip detection. Seventy-two percent to 80% of outdoor walking/running minutes, 73% of bicycling minutes, and 74%–76% of in-vehicle minutes were correctly classified by PALMS. For minutes/day, PALMS had a mean bias (i.e., amount of over or under estimation) of 2.4–3.1 min (11%–15%) for walking/running, 2.3–2.9 min (7%–9%) for bicycling, and 4.3–5 min (15%–17%) for vehicle time. Intraclass correlation coefficients were Q0.80 for all modes. Conclusions: PALMS has validity for processing GPS data to objectively measure time spent walking/running, bicycling, and in vehicle in population studies. Assessing travel patterns is one of many valuable applications of GPS in physical activity research that can improve our understanding of the determinants and health outcomes of active transportation as well as its effect on physical activity.",
keywords = "bicycling, geography, Physical activity, transportation, vehicle, walking",
pubstate = "published",
tppubtype = "article"
}

@inproceedings{Lin2014,
title = "MMap: Fast billion-scale graph computation on a PC via memory mapping",
author = "Z. Lin and M. Kahng and K.M. Sabrin and D.H. Chau and H. Lee and U. Kang",
url = "http://www.cc.gatech.edu/~dchau/papers/14-bigdata-mmap.pdf",
year = 2014,
date = "2014-10-27",
booktitle = "2014 IEEE International Conference on Big Data",
pages = "159--164",
organization = "IEEE",
abstract = "Graph computation approaches such as GraphChi and TurboGraph recently demonstrated that a single PC can perform efficient computation on billion-node graphs. To achieve high speed and scalability, they often need sophisticated data structures and memory management strategies. We propose a minimalist approach that forgoes such requirements, by leveraging the fundamental memory mapping (MMap) capability found on operating systems. We contribute: (1) a new insight that MMap is a viable technique for creating fast and scalable graph algorithms that surpasses some of the best techniques; (2) the design and implementation of popular graph algorithms for billion-scale graphs with little code, thanks to memory mapping; (3) extensive experiments on real graphs, including the 6.6 billion edge YahooWeb graph, and show that this new approach is significantly faster or comparable to the highly-optimized methods (e.g., 9.5X faster than GraphChi for computing PageRank on 1.47B edge Twitter graph). We believe our work provides a new direction in the design and development of scalable algorithms. Our packaged code is available at http://poloclub.gatech.edu/mmap/.",
keywords = "data mining, graph computation, GraphChi, MMap, scalable graph algorithms, TurboGraph",
pubstate = "published",
tppubtype = "inproceedings"
}

@inproceedings{Chen2014,
title = "Towards scalable graph computation on mobile devices",
author = "Y. Chen and Z. Lin and R. Pienta and M. Kahng and D.H. Chau",
url = "http://www.cc.gatech.edu/~dchau/papers/14-bigdata-mobile-mmap.pdf",
year = 2014,
date = "2014-10-27",
booktitle = "2014 IEEE International Conference on Big Data (Big Data),",
pages = "29--35",
organization = "IEEE",
abstract = "Mobile devices have become increasingly central to our everyday activities, due to their portability, multi-touch capabilities, and ever-improving computational power. Such attractive features have spurred research interest in leveraging mobile devices for computation. We explore a novel approach that aims to use a single mobile device to perform scalable graph computation on large graphs that do not fit in the device’s limited main memory, opening up the possibility of performing on-device analysis of large datasets, without relying on the cloud. Based on the familiar memory mapping capability provided by today’s mobile operating systems, our approach to scale up computation is powerful and intentionally kept simple to maximize its applicability across the iOS and Android platforms. Our experiments demonstrate that an iPad mini can perform fast computation on large real graphs with as many as 272 million edges (Google+ social graph), at a speed that is only a few times slower than a 13” Macbook Pro. Through creating a real world iOS app with this technique, we demonstrate the strong potential application for scalable graph computation on a single mobile device using our approach.",
keywords = "graph mining, memory mapping, mobile device, scalable algorithms",
pubstate = "published",
tppubtype = "inproceedings"
}

@article{Brennan2014,
title = "Patient-centered care, collaboration, communication, and coordination: a report from AMIA's 2013 Policy Meeting",
author = "P.F. Brennan and R. Valdez and G. Alexander and S. Arora and E.V. Bernstam and M. Edmunds and N. Kirienko and R.D. Martin and I. Sim and D. Skiba and T. Rosenbloom",
url = "http://jamia.oxfordjournals.org/content/early/2014/11/07/amiajnl-2014-003176",
year = 2014,
date = "2014-10-30",
journal = "Journal of the American Medical Informatics Association",
pages = "amiajnl--2014",
publisher = "BMJ Publishing Group Ltd",
abstract = "n alignment with a major shift toward patient-centered care as the model for improving care in our health system, informatics is transforming patient–provider relationships and overall care delivery. AMIA's 2013 Health Policy Invitational was focused on examining existing challenges surrounding full engagement of the patient and crafting a research agenda and policy framework encouraging the use of informatics solutions to achieve this goal. The group tackled this challenge from educational, technical, and research perspectives. Recommendations include the need for consumer education regarding rights to data access, the need for consumers to access their health information in real time, and further research on effective methods to engage patients. This paper summarizes the meeting as well as the research agenda and policy recommendations prioritized among the invited experts and stakeholders.",
keywords = "data privacy, health information technology, health policy, patient engagement, patient-centered care",
pubstate = "published",
tppubtype = "article"
}

@inproceedings{Zaman:2014:KSL:2676431.2676433,
title = "Kinematic-based Sedentary and Light-intensity Activity Detection for Wearable Medical Applications",
author = "K.I. Zaman and S.R. Yli-Piipari and T.W. Hnat",
url = "http://doi.acm.org/10.1145/2676431.2676433",
isbn = "978-1-4503-3190-6",
year = 2014,
date = "2014-11-03",
booktitle = "Proceedings of the 1st Workshop on Mobile Medical Applications",
pages = "28--33",
publisher = "ACM",
address = "Memphis, Tennessee",
series = "MMA '14",
abstract = "A sedentary lifestyle is becoming common for many individuals throughout the United States; however, this comes with a health cost of various preventable diseases such as cardiovascular disease, colon cancer, metabolic syndrome, and diabetes. Many times, individuals are completely unaware of how his or her health has deteriorated because of the slow progression or the demands of a job. We seek to bring attention to these problems by identifying specific sedentary activities and propose that just-in-time interventions could be used to help individuals overcome some of these problems. Our solution involves wearable sensors and utilizes a kinematic-based activity recognition systems to identify sedentary and light-intensity activities. Our system is evaluated with a series of laboratory experiments that include data from 34 individuals and a total of over 1400 minutes of activity. Results indicate that our system has a classification accuracy of up to 95.4 percent across all activities.",
keywords = "Body Sensor Network, Kinematics",
pubstate = "published",
tppubtype = "inproceedings"
}

@inproceedings{Ouyang2014,
title = "Truth Discovery in Crowdsourced Detection of Spatial Events",
author = "R.W. Ouyang and M.B. Srivastava and A. Toniolo and T.J. Norman",
url = "http://dl.acm.org/citation.cfm?id=2662003",
year = 2014,
date = "2014-11-03",
booktitle = "Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management",
pages = "461--470",
organization = "ACM",
abstract = "The ubiquity of smartphones has led to the emergence of mobile crowdsourcing tasks such as the detection of spatial events when smartphone users move around in their daily lives. However, the credibility of those detected events can be negatively impacted by unreliable participants with low-quality data. Consequently, a major challenge in quality control is to discover true events from diverse and noisy participants’ reports. This truth discovery problem is uniquely distinct from its online counterpart in that it involves uncertainties in both participants’ mobility and reliability. Decoupling these two types of uncertainties through location tracking will raise severe privacy and energy issues, whereas simply ignoring missing reports or treating them as negative reports will significantly degrade the accuracy of the discovered truth. In this paper, we propose a new method to tackle this truth discovery problem through principled probabilistic modeling. In particular, we integrate the modeling of location popularity, location visit indicators, truth of events and three-way participant reliability in a unified framework. The proposed model is thus capable of efficiently handling various types of uncertainties and automatically discovering truth without any supervision or the need of location tracking. Experimental results demonstrate that our proposed method outperforms existing state-of-the-art truth discovery approaches in the mobile crowdsourcing environment.",
keywords = "Graphical Models, Mobile crowdsourcing, quality control",
pubstate = "published",
tppubtype = "inproceedings"
}

@inproceedings{Martin2014a,
title = "Social spring: encounter-based path refinement for indoor tracking systems",
author = "P. Martin and Y. Shoukry and P. Swaminathan and R.W. Ouyang and M.B. Srivastava",
url = "http://dl.acm.org/citation.cfm?id=2674061.2674065",
year = 2014,
date = "2014-11-03",
booktitle = "Proceedings of the 1st ACM Conference on Embedded Systems for Energy-Efficient Buildings (ACM BuildSys 2014)",
pages = "156--159",
organization = "ACM",
abstract = "Indoor localization is poised to catalyze the development of smarter buildings and the creation of reactive indoor spaces, allowing for user-optimized energy expenditure and a more intimate user experience. This paper presents Social Spring, an architecture and corresponding software suite for refining indoor path estimation algorithms. Given an underlying indoor localization scheme, Social Spring attempts to reduce path estimation errors by leveraging encounters between users in real time. The driving concept behind Social Spring is that paths are treated as strings of nodes connected edgewise in a graph, while encounters are treated as additional edges in that graph. Each node attempts to minimize a local potential function dictated by a network of springs, the minimum of which is designed such that nodes converge to a lower energy equivalently lower error) state. We further provide simulations and preliminary tests on real indoor localization datasets in order to lend credence to Social Spring’s effectiveness over a range of environmental factors, demonstrating between 10% and 30% error reduction.",
keywords = "Encounters, Graph Realization, Indoor Localization, Positioning",
pubstate = "published",
tppubtype = "inproceedings"
}

J Bidwell, I A Essa, A Rozga and G D Abowd. Measuring Child Visual Attention using Markerless Head Tracking from Color and Depth Sensing Cameras. In Proceedings of the 16th International Conference on Multimodal Interaction (ICMI '14). 2014, 447–454. URLBibTeX

@inproceedings{Bidwell2014,
title = "Measuring Child Visual Attention using Markerless Head Tracking from Color and Depth Sensing Cameras",
author = "J. Bidwell and I.A. Essa and A. Rozga and G.D. Abowd",
url = "http://dl.acm.org/citation.cfm?id=2663235",
year = 2014,
date = "2014-11-12",
booktitle = "Proceedings of the 16th International Conference on Multimodal Interaction (ICMI '14)",
pages = "447--454",
organization = "ACM",
abstract = "A child's failure to respond to his or her name being called is an early warning sign for autism and response to name is currently assessed as a part of standard autism screening and diagnostic tools. In this paper, we explore markerless child head tracking as an unobtrusive approach for automatically predicting child response to name. Head turns are used as a proxy for visual attention. We analyzed 50 recorded response to name sessions with the goal of predicting if children, ages 15 to 30 months, responded to name calls by turning to look at an examiner within a defined time interval. The child's head turn angles and hand annotated child name call intervals were extracted from each session. Human assisted tracking was employed using an overhead Kinect camera, and automated tracking was later employed using an additional forward facing camera as a proof-of-concept. We explore two distinct analytical approaches for predicting child responses, one relying on rule-based approached and another on random forest classification. In addition, we derive child response latency as a new measurement that could provide researchers and clinicians with finer grain quantitative information currently unavailable in the field due to human limitations. Finally we reflect on steps for adapting our system to work in less constrained natural settings.",
keywords = "Algorithms, Computational Behavioral Analysis; Autism Spectrum Disorder, Experimentation, Human Factors",
pubstate = "published",
tppubtype = "inproceedings"
}

@article{chakraborty2014dynamic,
title = "Dynamic treatment regimes",
author = "B. Chakraborty and S.A. Murphy",
url = "http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4231831/",
year = 2014,
date = "2014-11-14",
journal = "Annual Review of Statistics and Its Application",
volume = 1,
pages = "447-464",
publisher = "NIH Public Access",
abstract = "A dynamic treatment regime consists of a sequence of decision rules, one per stage of intervention, that dictate how to individualize treatments to patients based on evolving treatment and covariate history. These regimes are particularly useful for managing chronic disorders, and fit well into the larger paradigm of personalized medicine. They provide one way to operationalize a clinical decision support system. Statistics plays a key role in the construction of evidence-based dynamic treatment regimes  informing best study design as well as efficient estimation and valid inference. Due to the many novel methodological challenges it offers, this area has been growing in popularity among statisticians in recent years. In this article, we review the key developments in this exciting field of research. In particular, we discuss the sequential multiple assignment randomized trial designs, estimation techniques like Q-learning and marginal structural models, and several inference techniques designed to address the associated non-standard asymptotics. We reference software, whenever available. We also outline some important future directions.",
keywords = "dynamic treatment regime, non-regularity, Q-learning, reinforcement learning, sequential randomization",
pubstate = "published",
tppubtype = "article"
}

@article{Sim2014,
title = "The Ontology of Clinical Research (OCRe): an informatics foundation for the science of clinical research.",
author = "I. Sim and S.W. Tu and S. Carini and H.P. Lehmann and B.H. Pollock and M. Peleg and K.M. Wittkowski",
url = "http://dx.doi.org/10.1016/j.jbi.2013.11.002",
year = 2014,
date = "2014-12-01",
journal = "Journal of Biomedical Informatics",
volume = 52,
pages = "78--91",
institution = "Department of Research Design and Biostatistics, The Rockefeller University, New York, NY, United States.",
abstract = "To date, the scientific process for generating, interpreting, and applying knowledge has received less informatics attention than operational processes for conducting clinical studies. The activities of these scientific processes - the science of clinical research - are centered on the study protocol, which is the abstract representation of the scientific design of a clinical study. The Ontology of Clinical Research (OCRe) is an OWL 2 model of the entities and relationships of study design protocols for the purpose of computationally supporting the design and analysis of human studies. OCRe's modeling is independent of any specific study design or clinical domain. It includes a study design typology and a specialized module called ERGO Annotation for capturing the meaning of eligibility criteria. In this paper, we describe the key informatics use cases of each phase of a study's scientific lifecycle, present OCRe and the principles behind its modeling, and describe applications of OCRe and associated technologies to a range of clinical research use cases. OCRe captures the central semantics that underlies the scientific processes of clinical research and can serve as an informatics foundation for supporting the entire range of knowledge activities that constitute the science of clinical research.",
keywords = "clinical research, informatics, Ontology of Clinical Research, OWL2, study design",
pubstate = "published",
tppubtype = "article"
}

@article{Rebar2014a,
title = "Intention-behavior gap is wider for walking and moderate physical activity than for vigorous physical activity in university students.",
author = "A.L. Rebar and J.P. Maher and S.E. Doerksen and S. Elavsky and D.E. Conroy",
url = "http://dx.doi.org/10.1016/j.jsams.2014.11.392",
year = 2014,
date = "2014-12-05",
journal = "Journal of Science and Medicine in Sport",
institution = "Center for Behavior and Health - Institute for Public Health and Medicine, Northwestern University, USA.",
abstract = "The theory of planned behavior proposes that physical activity is the result of intentions; however little is known about whether the relation between intentions and behavior differs between vigorous, moderate physical activity, and walking. For university students, vigorous physical activity is oftentimes enacted as a goal-directed behavior; whereas walking is oftentimes a means to achieving a goal other than physical activity (e.g., transportation).The study was a one-week prospective study.Undergraduate students (N=164) reported intentions for walking, moderate physical activity, and vigorous physical activity and self-reported these behaviors one week later.Hierarchical linear modeling revealed that intentions were more strongly related to vigorous physical activity than to moderate physical activity or walking.Intention-enhancing interventions may effectively promote vigorous physical activity, but other motivational processes may be more appropriate to target in interventions of walking and moderate physical activity.",
keywords = "Exercise, Motivation, Physical activity intensity, Theory of planned behavior",
pubstate = "published",
tppubtype = "article"
}

@inproceedings{rahman2011mconverse,
author = "Rahman, Md Mahbubur and Ali, Amin Ahsan and Plarre, Kurt and Al'Absi, Mustafa and Ertin, Emre and Kumar, Santosh",
title = "mconverse: Inferring conversation episodes from respiratory measurements collected in the field",
booktitle = "Proceedings of the 2nd Conference on Wireless Health",
year = 2011,
pages = 10,
organization = "ACM",
abstract = "Automated detection of social interactions in the natural environment has resulted in promising advances in organizational behavior, consumer behavior, and behavioral health. Progress, however, has been limited since the primary means of assessing social interactions today (i.e., audio recording) has several issues in field usage such as microphone occlusion, lack of speaker specificity, and high energy drain, in addition to significant privacy concerns. In this paper, we present mConverse, a new mobilebased system to infer conversation episodes from respiration measurements collected in the field from an unobtrusively wearable respiratory inductive plethysmograph (RIP) band worn around the user’s chest. The measurements are wirelessly transmitted to a mobile phone, where they are used in a novel machine learning model to determine whether the wearer is speaking, listening, or quiet. Our model incorporates several innovations to address issues that naturally arise in the noisy field environment such as confounding events, poor data quality due to sensor loosening and detachment, losses in the wireless channel, etc. Our basic model obtains 83% accuracy for the three class classification. We formulate a Hidden Markov Model to further improve the accuracy to 87%. Finally, we apply our model to data collected from 22 subjects who wore the sensor for 2 full days in the field to observe conversation behavior in daily life and find that people spend 25% of their day in conversations"
}

@inproceedings{Ertin:2011:AUW:2070942.2070970,
author = "Ertin, Emre and Stohs, Nathan and Kumar, Santosh and Raij, Andrew and al'Absi, Mustafa and Shah, Siddharth",
title = "AutoSense: Unobtrusively Wearable Sensor Suite for Inferring the Onset, Causality, and Consequences of Stress in the Field",
booktitle = "Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems",
year = 2011,
series = "SenSys '11",
pages = "274--287",
address = "New York, NY, USA",
publisher = "ACM",
abstract = "The effect of psychosocial stress on health has been a central focus area of public health research. However, progress has been limited due a to lack of wearable sensors that can provide robust measures of stress in the field. In this paper, we present a wireless sensor suite called AutoSense that collects and processes cardiovascular, respiratory, and thermoregularity measurements that can inform about the general stress state of test subjects in their natural environment. AutoSense overcomes several challenges in the design of wearable sensor systems for use in the field. First, it is unobtrusively wearable because it integrates six sensors in a small form factor. Second, it demonstrates a low power design; with a lifetime exceeding ten days while continuously sampling and transmitting sensor measurements. Third, sensor measurements are robust to several sources of errors and confounds inherent in field usage. Fourth, it integrates an ANT radio for low power and integrated quality of service guarantees, even in crowded environments. The AutoSense suite is complemented with a software framework on a smart phone that processes sensor measurements received from AutoSense to infer stress and other rich human behaviors. AutoSense was used in a 20+ subject real-life scientific study on stress in both the lab and field, which resulted in the first model of stress that provides 90% accuracy.",
acmid = 2070970,
doi = "10.1145/2070942.2070970",
isbn = "978-1-4503-0718-5",
keywords = "deployment experiences, mobile health, psychological stress monitoring, wearable physiological sensors",
location = "Seattle, Washington",
numpages = 14,
url = "http://doi.acm.org/10.1145/2070942.2070970"
}

mHealthHUB is a service of the Center of Excellence for Mobile Sensor Data-to-Knowledge (MD2K). MD2K , headquartered at the University of Memphis, is supported by the National Institutes of Health Big Data to Knowledge (BD2K) Initiative Grant #1U54EB020404